Compare commits
1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Hykilpikonna
|
535cc49ccc |
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# Auto detect text files and perform LF normalization
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* text=auto
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-131
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# Byte-compiled / optimized / DLL files
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||||
__pycache__/
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*.py[cod]
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*$py.class
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||||
|
||||
# C extensions
|
||||
*.so
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||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
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||||
dist/
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||||
downloads/
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||||
eggs/
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||||
.eggs/
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||||
lib/
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||||
lib64/
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||||
parts/
|
||||
sdist/
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||||
var/
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||||
wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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||||
# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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||||
*.spec
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||||
|
||||
# Installer logs
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||||
pip-log.txt
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pip-delete-this-directory.txt
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||||
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||||
# Unit test / coverage reports
|
||||
htmlcov/
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.tox/
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.nox/
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||||
.coverage
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.coverage.*
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.cache
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||||
nosetests.xml
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coverage.xml
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||||
*.cover
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.hypothesis/
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.pytest_cache/
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||||
# Translations
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||||
*.mo
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||||
*.pot
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||||
|
||||
# Django stuff:
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||||
*.log
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||||
local_settings.py
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||||
db.sqlite3
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||||
|
||||
# Flask stuff:
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||||
instance/
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||||
.webassets-cache
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||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
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||||
|
||||
# Sphinx documentation
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||||
docs/_build/
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||||
|
||||
# PyBuilder
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target/
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||||
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||||
# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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||||
# pyenv
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.python-version
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||||
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||||
# celery beat schedule file
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celerybeat-schedule
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||||
|
||||
# SageMath parsed files
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||||
*.sage.py
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||||
|
||||
# Environments
|
||||
.env
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||||
.venv
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||||
env/
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venv/
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ENV/
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||||
env.bak/
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venv.bak/
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||||
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||||
# Spyder project settings
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||||
.spyderproject
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||||
.spyproject
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||||
|
||||
# Rope project settings
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||||
.ropeproject
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||||
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||||
# mkdocs documentation
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||||
/site
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||||
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# Latex intermediate files
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||||
*.aux
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*.fdb_latexmk
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*.fls
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||||
*.gz
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||||
*.bbl
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*.blg
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*.bcf
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*.xml
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*.out
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||||
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config.json5
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data/
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/report/
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/src/report
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.DS_Store
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Generated
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||||
# Default ignored files
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||||
/shelf/
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||||
/workspace.xml
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||||
# Datasource local storage ignored files
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/dataSources/
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||||
/dataSources.local.xml
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# Editor-based HTTP Client requests
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||||
/httpRequests/
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-44
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<component name="InspectionProjectProfileManager">
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<profile version="1.0">
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<option name="myName" value="Project Default" />
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<inspection_tool class="JavaDoc" enabled="true" level="WARNING" enabled_by_default="true">
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<option name="TOP_LEVEL_CLASS_OPTIONS">
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<value>
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||||
<option name="ACCESS_JAVADOC_REQUIRED_FOR" value="none" />
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||||
<option name="REQUIRED_TAGS" value="" />
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</value>
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||||
</option>
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<option name="INNER_CLASS_OPTIONS">
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<value>
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<option name="ACCESS_JAVADOC_REQUIRED_FOR" value="none" />
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||||
<option name="REQUIRED_TAGS" value="" />
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</value>
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||||
</option>
|
||||
<option name="METHOD_OPTIONS">
|
||||
<value>
|
||||
<option name="ACCESS_JAVADOC_REQUIRED_FOR" value="none" />
|
||||
<option name="REQUIRED_TAGS" value="@return@param@throws or @exception" />
|
||||
</value>
|
||||
</option>
|
||||
<option name="FIELD_OPTIONS">
|
||||
<value>
|
||||
<option name="ACCESS_JAVADOC_REQUIRED_FOR" value="none" />
|
||||
<option name="REQUIRED_TAGS" value="" />
|
||||
</value>
|
||||
</option>
|
||||
<option name="IGNORE_DEPRECATED" value="false" />
|
||||
<option name="IGNORE_JAVADOC_PERIOD" value="true" />
|
||||
<option name="IGNORE_DUPLICATED_THROWS" value="false" />
|
||||
<option name="IGNORE_POINT_TO_ITSELF" value="false" />
|
||||
<option name="myAdditionalJavadocTags" value="date" />
|
||||
</inspection_tool>
|
||||
<inspection_tool class="JpaDataSourceORMInspection" enabled="false" level="ERROR" enabled_by_default="false" />
|
||||
<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
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||||
<option name="ignoredIdentifiers">
|
||||
<list>
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||||
<option value="bins" />
|
||||
</list>
|
||||
</option>
|
||||
</inspection_tool>
|
||||
</profile>
|
||||
</component>
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Generated
-6
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK">
|
||||
<output url="file://$PROJECT_DIR$/out" />
|
||||
</component>
|
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</project>
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Generated
-8
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<?xml version="1.0" encoding="UTF-8"?>
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||||
<project version="4">
|
||||
<component name="ProjectModuleManager">
|
||||
<modules>
|
||||
<module fileurl="file://$PROJECT_DIR$/CSC110-Project.iml" filepath="$PROJECT_DIR$/CSC110-Project.iml" />
|
||||
</modules>
|
||||
</component>
|
||||
</project>
|
||||
Generated
-6
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||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
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||||
<component name="VcsDirectoryMappings">
|
||||
<mapping directory="$PROJECT_DIR$" vcs="Git" />
|
||||
</component>
|
||||
</project>
|
||||
@@ -1,12 +0,0 @@
|
||||
cff-version: 1.2.0
|
||||
message: "If you use this software, please cite it as below."
|
||||
authors:
|
||||
- family-names: Gui
|
||||
given-names: Azalea
|
||||
orcid: https://orcid.org/0000-0002-6141-5926
|
||||
- family-names: Lin
|
||||
given-names: Peter
|
||||
title: "COVID-19 Twitter Posting Frequency and Popularity Insights"
|
||||
version: 1.0.0
|
||||
doi: TODO
|
||||
date-released: TODO
|
||||
@@ -1,14 +0,0 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<module type="PYTHON_MODULE" version="4">
|
||||
<component name="NewModuleRootManager" inherit-compiler-output="true">
|
||||
<exclude-output />
|
||||
<content url="file://$MODULE_DIR$">
|
||||
<sourceFolder url="file://$MODULE_DIR$/src" isTestSource="false" />
|
||||
<excludeFolder url="file://$MODULE_DIR$/data" />
|
||||
</content>
|
||||
<orderEntry type="inheritedJdk" />
|
||||
<orderEntry type="sourceFolder" forTests="false" />
|
||||
<orderEntry type="library" name="marked" level="application" />
|
||||
<orderEntry type="library" name="jquery" level="application" />
|
||||
</component>
|
||||
</module>
|
||||
@@ -1,21 +0,0 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2021 Hykilpikonna
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -1,4 +0,0 @@
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# CSC110-Project
|
||||
|
||||
Our final project for CSC110! This is a study on the frequency and popularity of COVID-19-related discussions on Twitter.
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Full paper hosted on https://csc110.hydev.org.
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||||
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||||
#!/usr/bin/env sh
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||||
|
||||
# abort on errors
|
||||
set -e
|
||||
|
||||
cp ../data/packed/processed.7z ./src/dist/processed-data.7z
|
||||
|
||||
# navigate into the build output directory
|
||||
cd src/dist
|
||||
|
||||
# if you are deploying to a custom domain
|
||||
echo 'csc110.hydev.org' > CNAME
|
||||
|
||||
git init
|
||||
git add -A
|
||||
git commit -m 'deploy'
|
||||
|
||||
# if you are deploying to https://<USERNAME>.github.io/<REPO>
|
||||
git push -f git@github.com:Hykilpikonna/CSC110-Project.git master:gh-pages
|
||||
|
||||
cd -
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||||
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||||
| Username | Frequency |
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||||
|-----------------|-------------|
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||||
| Beyonce | 50.0% |
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||||
| UNICEF | 38.9% |
|
||||
| WHO | 31.3% |
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||||
| EmmaWatson | 26.3% |
|
||||
| BillGates | 19.6% |
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||||
| UN | 19.5% |
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||||
| SenWarren | 18.4% |
|
||||
| XHNews | 14.7% |
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||||
| cnnbrk | 14.3% |
|
||||
| PDChina | 13.7% |
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||||
| BBCBreaking | 13.1% |
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||||
| BreakingNews | 11.4% |
|
||||
| AP | 11.3% |
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||||
| Caradelevingne | 11.1% |
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||||
| FLOTUS45 | 10.7% |
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||||
| sardesairajdeep | 10.7% |
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||||
| hazardeden10 | 10.5% |
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||||
| VP | 10.4% |
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||||
| POTUS | 10.0% |
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||||
| WSJ | 10.0% |
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||||
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||||
| Username | Frequency |
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||||
|-----------------|-------------|
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||||
| JHUCAIH | 54.8% |
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||||
| DrJudyMonroe | 49.6% |
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||||
| PoltergeistTC | 41.6% |
|
||||
| _FatmaAhmed | 31.6% |
|
||||
| OUCitizenGovern | 28.6% |
|
||||
| btolchin | 27.0% |
|
||||
| AusHCPNG | 26.4% |
|
||||
| UNECEHLM | 23.6% |
|
||||
| PIBFactCheck | 20.6% |
|
||||
| gospeakyourmind | 20.1% |
|
||||
| RepHarley | 20.0% |
|
||||
| susancolehaley | 19.2% |
|
||||
| SNHDflu | 18.4% |
|
||||
| UrbanScholar1 | 18.2% |
|
||||
| william_mcinnes | 18.2% |
|
||||
| chrisfradkin | 18.1% |
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||||
| USEmbassyBW | 18.1% |
|
||||
| carlosex | 17.7% |
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||||
| tobiaskurth | 17.4% |
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||||
| georgesoros | 17.2% |
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||||
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| | `500-pop` | `500-rand` | `eng-news` |
|
||||
|-------------------------------|-------------|--------------|--------------|
|
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| Total users | 500 | 500 | 310 |
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||||
| Users who didn't post at all | 117 | 205 | 26 |
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||||
| Users who posted less than 1% | 288 | 313 | 57 |
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| Username | Frequency |
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||||
|----------------|-------------|
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||||
| big_picture | 77.8% |
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||||
| NBCNewsHealth | 71.9% |
|
||||
| Circa | 50.0% |
|
||||
| caitlinnowens | 41.4% |
|
||||
| msnbc_breaking | 40.0% |
|
||||
| UnivisionNews | 34.6% |
|
||||
| firstdraftnews | 27.7% |
|
||||
| NBCNewsNow | 27.0% |
|
||||
| itvpeston | 26.9% |
|
||||
| FaceTheNation | 26.1% |
|
||||
| telegraphnews | 25.8% |
|
||||
| LesterHoltNBC | 23.4% |
|
||||
| nytimesphoto | 22.2% |
|
||||
| boomlive_in | 22.2% |
|
||||
| mckquarterly | 22.1% |
|
||||
| straits_times | 19.4% |
|
||||
| cnbcevents | 18.5% |
|
||||
| TwitterMoments | 17.6% |
|
||||
| sciam | 17.5% |
|
||||
| LiceMovono | 17.3% |
|
||||
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|
||||
| | `500-pop` | `500-rand` | `eng-news` |
|
||||
|----------|-------------|--------------|--------------|
|
||||
| Mean | 3.0% | 4.4% | 8.6% |
|
||||
| StdDev | 4.9% | 7.2% | 8.9% |
|
||||
| Median | 1.3% | 1.5% | 7.6% |
|
||||
| IQR | 3.5% | 4.3% | 7.8% |
|
||||
| Q1 (25%) | 0.4% | 0.5% | 3.1% |
|
||||
| Q3 (75%) | 3.9% | 4.8% | 10.9% |
|
||||
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||||
| Username | Popularity Ratio |
|
||||
|-----------------|--------------------|
|
||||
| juniorbachchan | 3370.4% |
|
||||
| Google | 983.3% |
|
||||
| JeremyClarkson | 847.2% |
|
||||
| Ibra_official | 814.8% |
|
||||
| SteveMartinToGo | 510.5% |
|
||||
| khloekardashian | 453.6% |
|
||||
| thetanmay | 425.9% |
|
||||
| Sethrogen | 423.3% |
|
||||
| Jacksepticeye | 392.9% |
|
||||
| JeffreeStar | 386.7% |
|
||||
| lukebryan | 359.6% |
|
||||
| BCCI | 352.9% |
|
||||
| 50cent | 346.4% |
|
||||
| RockstarGames | 339.9% |
|
||||
| mipaltan | 331.7% |
|
||||
| ashleytisdale | 331.4% |
|
||||
| jk_rowling | 328.6% |
|
||||
| Ninja | 320.6% |
|
||||
| AmazingPhil | 313.3% |
|
||||
| jamieoliver | 311.1% |
|
||||
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||||
| Username | Popularity Ratio |
|
||||
|-----------------|--------------------|
|
||||
| CarlosTF50 | 1868.2% |
|
||||
| QueenHote1 | 1390.3% |
|
||||
| L_Cook865 | 1220.1% |
|
||||
| 1chiarajolie | 1136.9% |
|
||||
| jolun | 1025.4% |
|
||||
| Numb3z | 970.7% |
|
||||
| GEFURST | 828.3% |
|
||||
| AtkinsQC | 814.6% |
|
||||
| JZerucelli | 753.3% |
|
||||
| theAMshakeout | 673.6% |
|
||||
| shauna_louise0 | 614.5% |
|
||||
| camillembaker | 587.5% |
|
||||
| HappyWarriorP | 558.2% |
|
||||
| LouisaJamesITV | 556.8% |
|
||||
| wyomingwormboy | 544.0% |
|
||||
| dafydd_llewelyn | 506.5% |
|
||||
| _angemccormack | 446.9% |
|
||||
| DenchiSoft | 441.4% |
|
||||
| jennakubsnc | 424.8% |
|
||||
| damashreal | 420.3% |
|
||||
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||||
| Username | Popularity Ratio |
|
||||
|----------------|--------------------|
|
||||
| TwitterData | 702.6% |
|
||||
| empiremagazine | 371.4% |
|
||||
| CNBCPolitics | 370.1% |
|
||||
| TB_Times | 342.8% |
|
||||
| instyle | 342.7% |
|
||||
| weatherchannel | 314.1% |
|
||||
| NAHJ | 295.8% |
|
||||
| navikakumar | 278.9% |
|
||||
| Telegraph | 277.4% |
|
||||
| TwitterDC | 269.1% |
|
||||
| thedailybeast | 256.7% |
|
||||
| karaswisher | 242.7% |
|
||||
| dallasnews | 240.2% |
|
||||
| jbouie | 233.1% |
|
||||
| jonfortt | 229.9% |
|
||||
| DavidBegnaud | 216.3% |
|
||||
| NickKristof | 213.9% |
|
||||
| LiceMovono | 204.3% |
|
||||
| washingtonpost | 198.1% |
|
||||
| VICENews | 196.2% |
|
||||
@@ -0,0 +1,3 @@
|
||||
| | `500-pop` | `500-rand` | `eng-news` |
|
||||
|---------|-------------|--------------|--------------|
|
||||
| Ignored | 117 | 205 | 28 |
|
||||
@@ -0,0 +1,8 @@
|
||||
| | `500-pop` | `500-rand` | `eng-news` |
|
||||
|----------|-------------|--------------|--------------|
|
||||
| Mean | 1.08 | 1.48 | 1.02 |
|
||||
| StdDev | 1.99 | 2.15 | 0.72 |
|
||||
| Median | 0.73 | 0.93 | 0.88 |
|
||||
| IQR | 0.74 | 1.13 | 0.6 |
|
||||
| Q1 (25%) | 0.41 | 0.42 | 0.62 |
|
||||
| Q3 (75%) | 1.15 | 1.55 | 1.22 |
|
||||
@@ -0,0 +1,8 @@
|
||||
| | `500-pop` | `500-rand` | `eng-news` |
|
||||
|----------|-------------|--------------|--------------|
|
||||
| Mean | 0.78 | 0.98 | 0.91 |
|
||||
| StdDev | 0.52 | 0.8 | 0.46 |
|
||||
| Median | 0.69 | 0.87 | 0.87 |
|
||||
| IQR | 0.65 | 0.96 | 0.57 |
|
||||
| Q1 (25%) | 0.38 | 0.34 | 0.61 |
|
||||
| Q3 (75%) | 1.03 | 1.3 | 1.18 |
|
||||
Binary file not shown.
@@ -1,36 +0,0 @@
|
||||
|
||||
####################
|
||||
# Data Collection
|
||||
# Json5 is a human-readable json format that allows for things such as unquoted keys or comments.
|
||||
json5~=0.9.6
|
||||
# Tweepy is a python SDK for twitter
|
||||
tweepy~=4.4.0
|
||||
# requests is for getting html from a website URL
|
||||
requests~=2.26.0
|
||||
# beautifulsoup is used to extract data from html
|
||||
beautifulsoup4~=4.10.0
|
||||
|
||||
#####################
|
||||
# Data Visualization
|
||||
# Print table data
|
||||
tabulate~=0.8.9
|
||||
# Draw local graphs
|
||||
matplotlib~=3.5.1
|
||||
# Calculate data statistics
|
||||
numpy~=1.21.4
|
||||
# Date utility for manipulating dates
|
||||
python-dateutil~=2.8.2
|
||||
# Scipy for transforming data. We used it for IIR filtering.
|
||||
scipy~=1.7.3
|
||||
# For serving the report website
|
||||
flask~=2.0.2
|
||||
|
||||
####################
|
||||
# Data Packing
|
||||
# 7zip packing utility for packing our processed data
|
||||
py7zr~=0.17.0
|
||||
|
||||
#####################
|
||||
# Testing and code checking
|
||||
pytest
|
||||
python-ta
|
||||
@@ -0,0 +1,4 @@
|
||||
| | Total | English | Chinese | Japanese |
|
||||
|------------|---------|-----------|-----------|------------|
|
||||
| `500-pop` | 500 | 495 | 0 | 5 |
|
||||
| `500-rand` | 500 | 393 | 15 | 92 |
|
||||
@@ -1,56 +0,0 @@
|
||||
"""CSC110 Fall 2021 Project
|
||||
This module uses web requests to collect and process other data we are using in our analysis.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass
|
||||
|
||||
import requests
|
||||
import python_ta
|
||||
import python_ta.contracts
|
||||
|
||||
|
||||
@dataclass
|
||||
class CasesData:
|
||||
"""
|
||||
A dataclass that stores a mapping of date to cases on that day and a mapping of date to deaths
|
||||
on that day.
|
||||
|
||||
Attributes:
|
||||
- cases: cases[date in "YYYY-MM-DD"] = 7-day average of cases around that date
|
||||
- deaths: deaths[date in "YYYY-MM-DD"] = 7-day average of deaths around that date
|
||||
|
||||
Representation Invariants:
|
||||
- all(x >= 0 for x in self.cases.values())
|
||||
- all(x >= 0 for x in self.deaths.values())
|
||||
"""
|
||||
cases: dict[str, float]
|
||||
deaths: dict[str, float]
|
||||
|
||||
|
||||
def get_covid_cases_us() -> CasesData:
|
||||
"""
|
||||
Get the US COVID-19 cases data from https://github.com/nytimes/covid-19-data by New York Times
|
||||
|
||||
:return: Cases data
|
||||
"""
|
||||
url = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/rolling-averages/us.csv'
|
||||
csv = requests.get(url).text.replace('\r\n', '\n').split('\n')[1:]
|
||||
data = CasesData({}, {})
|
||||
|
||||
# Parse CSV
|
||||
for line in csv:
|
||||
split = line.split(',')
|
||||
day, cases, deaths = split[0], split[2], split[6]
|
||||
data.cases[day] = float(cases)
|
||||
data.deaths[day] = float(deaths)
|
||||
return data
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
python_ta.contracts.check_all_contracts()
|
||||
python_ta.check_all(config={
|
||||
'extra-imports': ['requests', 'dataclasses'], # the names (strs) of imported modules
|
||||
'allowed-io': [], # the names (strs) of functions that call print/open/input
|
||||
'max-line-length': 100,
|
||||
'disable': ['R1705', 'C0200']
|
||||
})
|
||||
@@ -1,315 +0,0 @@
|
||||
"""CSC110 Fall 2021 Project
|
||||
This module interacts directly with the Twitter API to download tweets and users.
|
||||
It contains functions related scraping users/tweets, including:
|
||||
- getting the tweets of a user
|
||||
- downloading many users by checking their followers and follower's followers, etc.
|
||||
"""
|
||||
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import time
|
||||
from typing import List, Union
|
||||
|
||||
import tweepy
|
||||
from tweepy import API, TooManyRequests, User, Tweet, Unauthorized, NotFound
|
||||
|
||||
import python_ta
|
||||
import python_ta.contracts
|
||||
|
||||
from constants import TWEETS_DIR, USER_DIR
|
||||
from utils import Config, debug, calculate_rate_delay, write, json_stringify, read
|
||||
|
||||
|
||||
def tweepy_login(conf: Config) -> tweepy.API:
|
||||
"""
|
||||
Login to tweepy
|
||||
|
||||
:param conf: Config from load_config()
|
||||
:return: Tweepy API object
|
||||
"""
|
||||
auth = tweepy.OAuthHandler(conf.consumer_key, conf.consumer_secret)
|
||||
auth.set_access_token(conf.access_token, conf.access_secret)
|
||||
api: tweepy.API = tweepy.API(auth)
|
||||
return api
|
||||
|
||||
|
||||
def get_tweets(api: API, name: str, rate_delay: float, max_id: Union[int, None]) -> List[Tweet]:
|
||||
"""
|
||||
Get tweets and wait for delay
|
||||
|
||||
:param api: Tweepy API object
|
||||
:param name: Screen name
|
||||
:param rate_delay: Seconds of delay per request
|
||||
:param max_id: Max id of the tweet or none
|
||||
:return: Tweets list
|
||||
"""
|
||||
tweets = api.user_timeline(screen_name=name, count=200, tweet_mode='extended', trim_user=True,
|
||||
max_id=max_id)
|
||||
time.sleep(rate_delay)
|
||||
return tweets
|
||||
|
||||
|
||||
def download_all_tweets(api: API, screen_name: str,
|
||||
download_if_exists: bool = False) -> None:
|
||||
"""
|
||||
Download all tweets from a specific individual to a local folder.
|
||||
|
||||
Data Directory
|
||||
--------
|
||||
It will download all tweets to ./data/twitter/user-tweets/user/<screen_name>.json
|
||||
|
||||
Twitter API Reference
|
||||
--------
|
||||
It will be using the API endpoint api.twitter.com/statuses/user_timeline (Documentation:
|
||||
https://developer.twitter.com/en/docs/twitter-api/v1/tweets/timelines/api-reference/get
|
||||
-statuses-user_timeline)
|
||||
This endpoint has a rate limit of 900 requests / 15-minutes = 60 rpm for user auth, and it has a
|
||||
limit of 100,000 requests / 24 hours = 69.44 rpm independent of authentication method. To be
|
||||
safe, this function uses a rate limit of 60 rpm.
|
||||
|
||||
:param api: Tweepy API object
|
||||
:param screen_name: Screen name of that individual
|
||||
:param download_if_exists: Whether to download if it already exists (Default: False)
|
||||
:return: None
|
||||
"""
|
||||
# Ensure directories exist
|
||||
file = f'{TWEETS_DIR}/user/{screen_name}.json'
|
||||
|
||||
# Check if user already exists
|
||||
if os.path.isfile(file):
|
||||
if download_if_exists:
|
||||
debug(f'!!! User tweets data for {screen_name} already exists, but overwriting.')
|
||||
else:
|
||||
debug(f'User tweets data for {screen_name} already exists, skipping.')
|
||||
return
|
||||
|
||||
debug(f'Downloading user tweets for {screen_name}')
|
||||
|
||||
# Rate limit for this endpoint is 60 rpm for user auth and 69.44 rpm for app auth.
|
||||
rate_delay = calculate_rate_delay(60)
|
||||
|
||||
# Get initial 200 tweets
|
||||
try:
|
||||
tweets = get_tweets(api, screen_name, rate_delay, None)
|
||||
except Unauthorized:
|
||||
debug(f'- {screen_name}: Unauthorized. Probably a private account, ignoring.')
|
||||
return
|
||||
except NotFound:
|
||||
debug(f'- {screen_name}: Not found. Probably a deleted account, ignoring.')
|
||||
return
|
||||
|
||||
# This person has no tweets, done. (By the way, we discovered that @lorde has no tweets but has
|
||||
# 7 million followers... wow!)
|
||||
if len(tweets) == 0:
|
||||
write(file, '[]')
|
||||
return
|
||||
|
||||
# Get additional tweets
|
||||
while True:
|
||||
# Try to get more tweets
|
||||
debug(f'- {screen_name}: {len(tweets)} tweets...')
|
||||
additional_tweets = get_tweets(api, screen_name, rate_delay, int(tweets[-1].id_str) - 1)
|
||||
|
||||
# No more tweets
|
||||
if len(additional_tweets) == 0:
|
||||
debug(f'- {screen_name}: {len(tweets)} tweets, no more tweets are available.\n')
|
||||
break
|
||||
|
||||
# Add tweets to the list
|
||||
tweets.extend(additional_tweets)
|
||||
|
||||
# Store in file
|
||||
# Even though we are not supposed to use internal fields, there aren't any efficient way of
|
||||
# obtaining the json without the field. Using t.__dict__ will include the API object, which
|
||||
# is not serializable.
|
||||
write(file, json_stringify([t._json for t in tweets]))
|
||||
|
||||
|
||||
def download_users_start(api: API, start_point: str, n: float = math.inf) -> None:
|
||||
"""
|
||||
This function downloads n Twitter users by using a friends-chain.
|
||||
|
||||
Since there isn't an API or a database with all Twitter users, we can't obtain a strict list
|
||||
of all Twitter users, nor can we obtain a list of strictly random or most popular Twitter
|
||||
users. Therefore, we use the method of follows chaining: we start from a specific individual,
|
||||
obtain their followers, and pick 6 random individuals from the friends list. Then, we repeat
|
||||
the process for the selected friends: we pick 6 random friends of the 6 random friends
|
||||
that we picked.
|
||||
|
||||
In reality, this method will be biased toward individuals that are worthy of following since
|
||||
"friends" are the list of users that someone followed.
|
||||
|
||||
Data Directory
|
||||
--------
|
||||
It will download all user data to ./data/twitter/user/users/<screen_name>.json
|
||||
It will save meta info to ./data/twitter/user/meta/
|
||||
|
||||
Twitter API Reference
|
||||
--------
|
||||
It will be using the API endpoint api.twitter.com/friends/list (Documentation:
|
||||
https://developer.twitter.com/en/docs/twitter-api/v1/accounts-and-users/follow-search-get-users/api-reference/get-friends-list)
|
||||
This will limit the rate of requests to 15 requests in a 15-minute window, which is one request
|
||||
per minute. But it is actually the fastest method of downloading a wide range of users on
|
||||
Twitter because it can download a maximum of 200 users at a time while the API for downloading
|
||||
a single user is limited to only 900 queries per 15, which is only 60 users per minute.
|
||||
|
||||
There is another API endpoint that might do the job, which is api.twitter.com/friends/ids (Doc:
|
||||
https://developer.twitter.com/en/docs/twitter-api/v1/accounts-and-users/follow-search-get-users/api-reference/get-friends-ids)
|
||||
However, even though this endpoint has a much higher request rate limit, it only returns user
|
||||
ids and not full user info.
|
||||
|
||||
Parameters
|
||||
--------
|
||||
:param api: Tweepy's API object
|
||||
:param start_point: Starting user's screen name.
|
||||
:param n: How many users do you want to download? (Default: math.inf)
|
||||
:return: None
|
||||
"""
|
||||
|
||||
# Set of all the downloaded users' screen names
|
||||
downloaded = set()
|
||||
|
||||
# The set of starting users that are queried.
|
||||
done_set = set()
|
||||
|
||||
# The set of starting users currently looping through
|
||||
current_set = {start_point}
|
||||
|
||||
# The next set of starting users
|
||||
next_set = set()
|
||||
|
||||
# Start download
|
||||
download_users_execute(api, n, downloaded,
|
||||
done_set, current_set, next_set)
|
||||
|
||||
|
||||
def download_users_resume_progress(api: API) -> None:
|
||||
"""
|
||||
Resume from started progress
|
||||
|
||||
:param api: Tweepy's API object
|
||||
:return: None
|
||||
"""
|
||||
# Open file and read
|
||||
meta = json.loads(read(f'{USER_DIR}/meta/meta.json'))
|
||||
|
||||
# Resume
|
||||
download_users_execute(api, meta['n'],
|
||||
set(meta['downloaded']), set(meta['done_set']),
|
||||
set(meta['current_set']), set(meta['next_set']))
|
||||
|
||||
|
||||
def download_users_execute(api: API, n: float,
|
||||
downloaded: set[str], done_set: set[str],
|
||||
current_set: set[str], next_set: set[str]) -> None:
|
||||
"""
|
||||
Execute download from the given parameters. The download method is defined in the document for
|
||||
the download_users function.
|
||||
|
||||
Resume functionality is necessary because twitter limits the rate of get friends list to 15
|
||||
requests in a 15-minute window, which is 1 request per minute, so it will take a long time to
|
||||
gather enough data, so we don't want to have to start over from the beginning once something
|
||||
goes wrong.
|
||||
|
||||
:param api: Tweepy's API object
|
||||
:param n: How many users do you want to download?
|
||||
:param downloaded: Set of all the downloaded users' screen names
|
||||
:param done_set: The set of starting users that are queried
|
||||
:param current_set: The set of starting users currently looping through
|
||||
:param next_set: The next set of starting users
|
||||
:return: None
|
||||
"""
|
||||
# Rate limit for this API endpoint is 1 request per minute, and rate delay defines how many
|
||||
# seconds to sleep for each request.
|
||||
rate_delay = calculate_rate_delay(1) + 1
|
||||
|
||||
print("Executing friends-chain download:")
|
||||
print(f"- n: {n}")
|
||||
print(f"- Requests per minute: {1}")
|
||||
print(f"- Directory: {USER_DIR}")
|
||||
print(f"- Downloaded: {len(downloaded)}")
|
||||
print(f"- Current search set: {len(current_set)}")
|
||||
print(f"- Next search set: {len(next_set)}")
|
||||
print()
|
||||
|
||||
# Loop until there are enough users
|
||||
while len(downloaded) < n:
|
||||
# Take a screen name from the current list
|
||||
screen_name = current_set.pop()
|
||||
|
||||
try:
|
||||
# Get a list of friends.
|
||||
friends: List[User] = api.get_friends(screen_name=screen_name, count=200)
|
||||
except TooManyRequests:
|
||||
# Rate limited, sleep and try again
|
||||
debug('Caught TooManyRequests exception: Rate limited, sleep and try again.')
|
||||
time.sleep(rate_delay)
|
||||
current_set.add(screen_name)
|
||||
continue
|
||||
|
||||
# Save users
|
||||
for user in friends:
|
||||
# This user was not saved, save the user.
|
||||
if user not in downloaded:
|
||||
# Save user json
|
||||
write(f'{USER_DIR}/users/{user.screen_name}.json', json_stringify(user._json))
|
||||
|
||||
# Add to set
|
||||
downloaded.add(user.screen_name)
|
||||
# debug(f'- Downloaded {user.screen_name}')
|
||||
|
||||
# Get users and their popularity that we haven't downloaded
|
||||
screen_names = [(u.screen_name, u.followers_count) for u in friends
|
||||
if u.screen_name not in done_set and not u.protected]
|
||||
|
||||
# Sort by followers count, from least popular to most popular
|
||||
screen_names.sort(key=lambda x: x[1])
|
||||
|
||||
# Add 3 random users to the next set
|
||||
# python_ta thinks that u is not indexable but it is, because it is a tuple of length 2
|
||||
if len(screen_names) > 3:
|
||||
samples = {u[0] for u in random.sample(screen_names, 3)}
|
||||
else:
|
||||
samples = {u[0] for u in screen_names}
|
||||
|
||||
# Add 3 most popular users that we haven't downloaded to the next set
|
||||
while len(screen_names) > 0 and len(samples) < 6:
|
||||
most_popular = screen_names.pop()[0]
|
||||
if most_popular not in done_set and most_popular not in samples:
|
||||
samples.add(most_popular)
|
||||
|
||||
# Add the selected users to the next set
|
||||
for s in samples:
|
||||
next_set.add(s)
|
||||
|
||||
# Change name lists
|
||||
if len(current_set) == 0:
|
||||
current_set = next_set
|
||||
next_set = set()
|
||||
|
||||
# This one is done
|
||||
done_set.add(screen_name)
|
||||
|
||||
# Update meta info so that downloading can be continued
|
||||
meta = {'downloaded': downloaded, 'done_set': done_set,
|
||||
'current_set': current_set, 'next_set': next_set, 'n': n}
|
||||
write(f'{USER_DIR}/meta/meta.json', json_stringify(meta))
|
||||
|
||||
debug(f'Finished saving friends of {screen_name}')
|
||||
debug(f'============= Total {len(downloaded)} saved =============')
|
||||
|
||||
# Rate limit
|
||||
time.sleep(rate_delay)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
python_ta.contracts.check_all_contracts()
|
||||
python_ta.check_all(config={
|
||||
'extra-imports': ['json', 'math', 'os', 'random', 'time', 'typing', 'tweepy', 'constants',
|
||||
'utils'], # the names (strs) of imported modules
|
||||
'allowed-io': ['download_users_execute'],
|
||||
'max-line-length': 100,
|
||||
'disable': ['R1705', 'C0200', 'R0913', 'W0212']
|
||||
}, output='pyta_report.html')
|
||||
@@ -1,8 +0,0 @@
|
||||
{
|
||||
// Twitter official V2 API keys
|
||||
// Register for them on the Twitter api portal
|
||||
consumer_key: 'Your_consumer_key',
|
||||
consumer_secret: 'Your_consumer_secret',
|
||||
access_token: 'Your_access_token',
|
||||
access_secret: 'Your_access_secret',
|
||||
}
|
||||
@@ -1,37 +0,0 @@
|
||||
"""CSC110 Fall 2021 Project
|
||||
This module stores constant variables in our projects.
|
||||
|
||||
Instructors said that we can use global constants: https://piazza.com/class/ksovzjrlsye72f?cid=1664
|
||||
|
||||
Note: Paths should not end with "/"
|
||||
"""
|
||||
|
||||
"""
|
||||
File structure:
|
||||
|
||||
data - Processed and raw data
|
||||
├── packed - Packed data
|
||||
└── twitter - Data obtained from Twitter
|
||||
├── user - Twitter user info data
|
||||
│ ├── meta - Meta-data about the follows-chain downloading progress
|
||||
│ ├── processed - Processed (filtered) user data.
|
||||
│ └── users - Raw user info, each json contains info of one user.
|
||||
└── user-tweets - Tweets data
|
||||
├── processed - Processed tweets.
|
||||
└── user - Raw tweets, each json contains all tweets from a user.
|
||||
|
||||
src - Source codes.
|
||||
├── report - Report content generated by report_all() @ visualization.py
|
||||
├── dist - Static website root generated by write_html() @ report.py
|
||||
└── resources - HTML static resources, some are hand-written and some are imported libraries.
|
||||
"""
|
||||
DATA_DIR = '../data'
|
||||
TWEETS_DIR = f'{DATA_DIR}/twitter/user-tweets'
|
||||
USER_DIR = f'{DATA_DIR}/twitter/user'
|
||||
REPORT_DIR = './report'
|
||||
RES_DIR = './resources'
|
||||
|
||||
# Debug mode, or developer mode. This affects two things:
|
||||
# 1. Whether debug messages are outputted
|
||||
# 2. Whether the web server regenerates the HTML page for every request
|
||||
DEBUG = True
|
||||
-89
@@ -1,89 +0,0 @@
|
||||
"""CSC110 Fall 2021 Project
|
||||
This module is the main module of our program which runs different functions in different modules
|
||||
by steps.
|
||||
"""
|
||||
|
||||
from collect_twitter import *
|
||||
from processing import *
|
||||
from report import *
|
||||
from utils import *
|
||||
from visualization import *
|
||||
|
||||
if __name__ == '__main__':
|
||||
#####################
|
||||
# Data collection - Step C1.0
|
||||
# Load config and create API
|
||||
# This is required if you would like to collect the data yourself.
|
||||
# In that case, you also need to register for Twitter API keys and add them to config.json5
|
||||
# conf = load_config('config.json5')
|
||||
# api = tweepy_login(conf)
|
||||
|
||||
#####################
|
||||
# Data collection - Step C1.1
|
||||
# Download a wide range of users from Twitter using follow-chaining starting from a single user.
|
||||
# (This task will never stop before it downloads every single user from twitter, so we need to
|
||||
# manually stop it when there are enough users)
|
||||
# download_users_start(api, 'voxdotcom')
|
||||
|
||||
# This task will run for a very, very long time to obtain a large dataset of Twitter users. If
|
||||
# you want to stop the process, you can resume it later using the following line:
|
||||
# download_users_resume_progress(api)
|
||||
|
||||
####################
|
||||
# Data collection - Step C1.2
|
||||
# Download all tweets from TwitterNews
|
||||
# download_all_tweets(api, 'TwitterNews')
|
||||
|
||||
#####################
|
||||
# Data processing - Step P1
|
||||
# (After step C1) Process the downloaded Twitter users, extract screen name, popularity, and
|
||||
# number of tweets data.
|
||||
# process_users()
|
||||
|
||||
#####################
|
||||
# Data processing - Step P2
|
||||
# (After step P1) Select 500 most popular users and 500 random users who meet a particular
|
||||
# criteria as our sample, also find news channels
|
||||
# select_user_sample()
|
||||
|
||||
#####################
|
||||
# Data collection - Step C2.1
|
||||
# (After step P2) Load the downloaded Twitter users by popularity, and start downloading all
|
||||
# tweets from 500 of the most popular users. Takes around 2 hours.
|
||||
# for u in load_user_sample().most_popular:
|
||||
# download_all_tweets(api, u.username)
|
||||
|
||||
#####################
|
||||
# Data collection - Step C2.2
|
||||
# (After step P2) Download all tweets from the 500 randomly selected users, takes around 2 hours
|
||||
# for u in load_user_sample().random:
|
||||
# download_all_tweets(api, u.username)
|
||||
|
||||
#####################
|
||||
# Data collection - Step C2.3
|
||||
# (After step P2) Download all tweets from the news channels we selected.
|
||||
# for u in load_user_sample().english_news:
|
||||
# download_all_tweets(api, u)
|
||||
# Filter out news channels that have been blocked by twitter or don't exist
|
||||
# filter_news_channels()
|
||||
|
||||
#####################
|
||||
# Data processing - Step P3
|
||||
# (After step C2) Process the downloaded tweets, determine whether they are covid-related
|
||||
# process_tweets()
|
||||
|
||||
####################
|
||||
# Data Visualization - Step V1
|
||||
# Generate all visualization reports and graphs
|
||||
report_all()
|
||||
|
||||
# Write HTML for deploying to GitHub Pages
|
||||
write_html()
|
||||
|
||||
# Serve webpage
|
||||
serve_report()
|
||||
|
||||
####################
|
||||
# Finalize the program for submission.
|
||||
# Pack processed and unprocessed data:
|
||||
# pack_data()
|
||||
@@ -1,395 +0,0 @@
|
||||
"""CSC110 Fall 2021 Project
|
||||
Processes data downloaded from the Twitter API. Processing consists of calculating popularity of
|
||||
users, creating samples of users, filtering news channels, and processing tweets for file storage.
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
import random
|
||||
import sys
|
||||
import zipfile
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import NamedTuple
|
||||
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
from py7zr import SevenZipFile
|
||||
|
||||
import python_ta
|
||||
|
||||
from constants import DATA_DIR, TWEETS_DIR, USER_DIR, RES_DIR
|
||||
from utils import read, debug, write, json_stringify
|
||||
|
||||
|
||||
class ProcessedUser(NamedTuple):
|
||||
"""
|
||||
User and popularity.
|
||||
|
||||
We use NamedTuple instead of dataclass because named tuples are easier to serialize in JSON, and
|
||||
they require much less space in the stored json format because no key info is stored. For
|
||||
example, using dataclass, the json for one UserPopularity object will be:
|
||||
{"username": "a", "popularity": 1, "num_postings": 1}, while using NamedTuple, the json will be:
|
||||
["a", 1, 1], which saves an entire 42 bytes for each user.
|
||||
|
||||
Attributes:
|
||||
- username: The Twitter user's screen name
|
||||
- popularity: A measurement of a user's popularity, such as followers count
|
||||
- num_postings: Number of tweets
|
||||
- language: Language code in Twitter's language code format
|
||||
|
||||
Representation Invariants:
|
||||
- self.username != ''
|
||||
"""
|
||||
username: str
|
||||
popularity: int
|
||||
num_postings: int
|
||||
lang: str
|
||||
|
||||
|
||||
def process_users() -> None:
|
||||
"""
|
||||
After downloading a wide range of users using download_users_start in raw_collect/twitter.py,
|
||||
this function will read the user files, extract only relevant information defined in the
|
||||
ProcessedUser class, and rank the users by popularity.
|
||||
|
||||
This function will save the processed user data to <user_dir>/processed/users.json
|
||||
|
||||
:return: None
|
||||
"""
|
||||
users = []
|
||||
|
||||
# Loop through all the files
|
||||
for filename in os.listdir(f'{USER_DIR}/users'):
|
||||
# Only check json files and ignore macOS dot files
|
||||
if filename.endswith('.json') and not filename.startswith('.'):
|
||||
# Read
|
||||
user = json.loads(read(f'{USER_DIR}/users/{filename}'))
|
||||
|
||||
# Get user language (The problem is, most people's lang field are null, so we have to
|
||||
# look at the language of their latest status as well, while they might not have a
|
||||
# status field as well!)
|
||||
lang = user['lang']
|
||||
status_lang = user['status']['lang'] if 'status' in user else None
|
||||
if lang is None:
|
||||
lang = status_lang
|
||||
|
||||
users.append(ProcessedUser(user['screen_name'], user['followers_count'],
|
||||
user['statuses_count'], lang))
|
||||
|
||||
# Log progress
|
||||
if len(users) % 2000 == 0:
|
||||
debug(f'Loaded {len(users)} users.')
|
||||
|
||||
# Sort by followers count, descending
|
||||
users.sort(key=lambda x: x.popularity, reverse=True)
|
||||
|
||||
# Save data
|
||||
write(f'{USER_DIR}/processed/users.json', json_stringify(users))
|
||||
|
||||
|
||||
def load_users() -> list[ProcessedUser]:
|
||||
"""
|
||||
Load processed user data after process_users
|
||||
|
||||
:return: List of processed users, sorted descending by popularity.
|
||||
"""
|
||||
return [ProcessedUser(*u) for u in json.loads(read(f'{USER_DIR}/processed/users.json'))]
|
||||
|
||||
|
||||
def get_user_popularity_ranking(user: str) -> int:
|
||||
"""
|
||||
Get a user's popularity ranking. This is not used in data analysis, just for curiosity.
|
||||
|
||||
:param user: Username
|
||||
:return: User's popularity ranking
|
||||
"""
|
||||
pop = load_users()
|
||||
for i in range(len(pop)):
|
||||
if pop[i].username == user:
|
||||
return i + 1
|
||||
return -1
|
||||
|
||||
|
||||
@dataclass()
|
||||
class UserSample:
|
||||
"""
|
||||
This is a data class storing our different samples.
|
||||
|
||||
Attributes:
|
||||
- most_popular: Our sample of the most popular users on Twitter
|
||||
- random: Our sample of random users on Twitter
|
||||
- english_news: Our sample of news media accounts on Twitter
|
||||
|
||||
Representation Invariants:
|
||||
- all(news != '' for news in self.english_news)
|
||||
"""
|
||||
most_popular: list[ProcessedUser]
|
||||
random: list[ProcessedUser]
|
||||
english_news: list[str]
|
||||
|
||||
|
||||
def select_user_sample() -> None:
|
||||
"""
|
||||
Select our sample of the 500 most popular users and 500 random users who meet the criteria. The
|
||||
criteria we use is that the user must have at least 150 followers, and must have a number of
|
||||
postings in between 1000 and 3250. Analyzing someone who don't post or someone who doesn't have
|
||||
enough followers for interaction might not reveal useful information. We also filter based on
|
||||
language, because we only know how to identify COVID-related posts in a few languages.
|
||||
|
||||
The result will be stored in <user_dir>/processed/sample.json
|
||||
|
||||
:return: None
|
||||
"""
|
||||
file = f'{USER_DIR}/processed/sample.json'
|
||||
|
||||
# Exists
|
||||
if os.path.isfile(file):
|
||||
debug(f'There is already a sample generated at {file}. If you want to reselect the'
|
||||
f'sample, please delete the existing sample file.')
|
||||
return
|
||||
|
||||
# Load users
|
||||
users = load_users()
|
||||
|
||||
# Filter by language first
|
||||
users = [u for u in users if u.lang is not None
|
||||
and any(lang in u.lang for lang in {'en', 'zh', 'ja'})]
|
||||
|
||||
# Find most popular, and exclude them from the random sample
|
||||
most_popular = users[:500]
|
||||
users = users[500:]
|
||||
|
||||
# Filter by criteria
|
||||
filtered = {u for u in users if 150 < u.popularity and 1000 < u.num_postings < 3250}
|
||||
debug(f'There are {len(filtered)} users who meets the criteria.')
|
||||
|
||||
# Sample
|
||||
sample = random.sample(filtered, 500)
|
||||
|
||||
# Save
|
||||
write(file, json_stringify(UserSample(most_popular, sample, get_english_news_channels())))
|
||||
|
||||
|
||||
def get_english_news_channels() -> list[str]:
|
||||
"""
|
||||
Find news channels that post in English from retweets of TwitterNews, combined with an
|
||||
established list of 100 most influential news channels reported by Nur Bermmen from memeburn.com
|
||||
|
||||
Run this after download_all_tweets(api, 'TwitterNews')
|
||||
|
||||
Preconditions:
|
||||
- <tweets_dir>/user/TwitterNews.json exists.
|
||||
|
||||
:return: A list of news channel screen names
|
||||
"""
|
||||
# Find news channels in retweets from TwitterNews
|
||||
news_channels = {'TwitterNews'}
|
||||
for tweet in json.loads(read(f'{TWEETS_DIR}/user/TwitterNews.json')):
|
||||
text: str = tweet['full_text']
|
||||
if text.startswith('RT @'):
|
||||
user = text[4:].split(':')[0]
|
||||
news_channels.add(user)
|
||||
|
||||
# Find news channels from top 100 list on memeburn.com
|
||||
url = 'https://memeburn.com/2010/09/the-100-most-influential-news-media-twitter-accounts/'
|
||||
soup = BeautifulSoup(requests.get(url).text, 'html.parser')
|
||||
users = {h.text[1:] for h in soup.select('table tr td:nth-child(2) > a')}
|
||||
|
||||
# Combine two sets, ignoring case (since the ids in the 100 list are all lowercase)
|
||||
news_channels_lower = {n.lower() for n in news_channels}
|
||||
for u in users:
|
||||
if u not in news_channels_lower:
|
||||
news_channels.add(u)
|
||||
|
||||
return list(news_channels)
|
||||
|
||||
|
||||
def filter_news_channels() -> None:
|
||||
"""
|
||||
Filter out news channels that don't exist anymore or have been banned by Twitter.
|
||||
|
||||
Preconditions:
|
||||
- Run this after downloading all tweets from the news channels in Step 2.3 in main.
|
||||
|
||||
:return: None
|
||||
"""
|
||||
sample = load_user_sample()
|
||||
for u in list(sample.english_news):
|
||||
u = u.lower()
|
||||
if not (os.path.isfile(f'{TWEETS_DIR}/processed/{u}.json')
|
||||
or os.path.isfile(f'{TWEETS_DIR}/users/{u}.json')):
|
||||
sample.english_news.remove(u)
|
||||
write(f'{USER_DIR}/processed/sample.json', json_stringify(sample))
|
||||
|
||||
|
||||
def load_user_sample() -> UserSample:
|
||||
"""
|
||||
Load the selected sample
|
||||
|
||||
:return: None
|
||||
"""
|
||||
j = json.loads(read(f'{USER_DIR}/processed/sample.json'))
|
||||
return UserSample([ProcessedUser(*u) for u in j['most_popular']],
|
||||
[ProcessedUser(*u) for u in j['random']],
|
||||
j['english_news'])
|
||||
|
||||
|
||||
class Posting(NamedTuple):
|
||||
"""
|
||||
Posting data stores the processed tweets' data, and it contains info such as whether a tweet is
|
||||
covid-related
|
||||
|
||||
Attributes:
|
||||
- covid_related: True if the post is determined to be covid-related
|
||||
- popularity: A measure of tweet popularity measured by comments + likes
|
||||
- repost: Whether the post is a repost
|
||||
- date: Posting date and time in ISO format ("YYYY-MM-DDThh-mm-ss")
|
||||
|
||||
Representation Invariants:
|
||||
- self.popularity >= 0
|
||||
"""
|
||||
covid_related: bool
|
||||
popularity: int
|
||||
repost: bool
|
||||
date: str
|
||||
|
||||
|
||||
def process_tweets() -> None:
|
||||
"""
|
||||
Process tweets, reduce the tweets' data to only a few fields defined in the Posting class. These
|
||||
include whether the tweet is covid-related, how popular is the tweet, if it is a repost, and its
|
||||
date. The processed tweet does not contain its content.
|
||||
|
||||
If a user's tweets is already processed, this function will skip over that user's data.
|
||||
|
||||
This function will save the processed tweets' data to <tweets_dir>/processed/<username>.json
|
||||
|
||||
:return: None
|
||||
"""
|
||||
# Loop through all the files
|
||||
for filename in os.listdir(f'{TWEETS_DIR}/user'):
|
||||
# Only check json files and ignore macOS dot files
|
||||
if filename.endswith('.json') and not filename.startswith('.'):
|
||||
# Check if already processed
|
||||
if os.path.isfile(f'{TWEETS_DIR}/processed/{filename}'):
|
||||
continue
|
||||
|
||||
# Read
|
||||
tweets = json.loads(read(f'{TWEETS_DIR}/user/{filename}'))
|
||||
p = [Posting(is_covid_related(t['full_text']),
|
||||
t['favorite_count'] + t['retweet_count'],
|
||||
'retweeted_status' in t,
|
||||
datetime.strptime(t['created_at'], '%a %b %d %H:%M:%S +0000 %Y')
|
||||
.isoformat())
|
||||
for t in tweets]
|
||||
|
||||
# Save data
|
||||
write(f'{TWEETS_DIR}/processed/{filename}', json_stringify(p))
|
||||
debug(f'Processed: {filename}')
|
||||
|
||||
|
||||
def load_tweets(username: str) -> list[Posting]:
|
||||
"""
|
||||
Load tweets for a specific user
|
||||
|
||||
:param username: User's screen name
|
||||
:return: User's processed tweets
|
||||
"""
|
||||
return [Posting(*p) for p in json.loads(read(
|
||||
os.path.join(TWEETS_DIR, f'processed/{username}.json')))]
|
||||
|
||||
|
||||
def is_covid_related(text: str) -> bool:
|
||||
"""
|
||||
Is a tweet / article covid-related. Currently, this is done through keyword matching. Even
|
||||
though we know that not all posts with covid-related words are covid-related posts, this is
|
||||
currently our best method of classification.
|
||||
|
||||
:param text: Text content
|
||||
:return: Whether the text is covid related
|
||||
"""
|
||||
# English
|
||||
# We're hesitant to include words like "pandemic" or "vaccine" because they might refer to other
|
||||
# pandemics or other vaccines. However, I think we need to include "the pandemic" because many
|
||||
# posts refer to covid only as "the pandemic."
|
||||
keywords = ['covid', 'the pandemic', 'lockdown', 'spikevax', 'comirnaty', 'vaxzevria',
|
||||
'coronavirus', 'moderna', 'pfizer', 'quarantine', 'vaccine', 'social distancing',
|
||||
'booster shot']
|
||||
|
||||
# Chinese
|
||||
keywords += ['新冠', '疫情', '感染', '疫苗', '隔离']
|
||||
|
||||
# Japanese
|
||||
keywords += ['コロナ', '検疫', '三密']
|
||||
|
||||
return any(k in text.lower() for k in keywords)
|
||||
|
||||
|
||||
def pack_data() -> None:
|
||||
"""
|
||||
This function packs processed data and raw data separately, and it also packs the data ready for
|
||||
submission on MarkUs
|
||||
|
||||
:return: None
|
||||
"""
|
||||
packed_dir = f'{DATA_DIR}/packed'
|
||||
Path(packed_dir).mkdir(parents=True, exist_ok=True)
|
||||
packed_data = f'{packed_dir}/processed.7z'
|
||||
packed_res = f'{packed_dir}/resources.7z'
|
||||
|
||||
# Pack processed data (Since packing this takes a long time, we decided to not overwrite it for
|
||||
# every run. This is also because the processed data hasn't changed since Nov 28 when the
|
||||
# project is mostly finished, and there is no need to re-pack data every time, whereas the
|
||||
# resources and sources might change after every update) So, delete the packed 7z file if you
|
||||
# want to repack.
|
||||
if not os.path.isfile(packed_data):
|
||||
debug('Packing data...')
|
||||
processed_dirs = ['/twitter/user/meta', '/twitter/user/processed',
|
||||
'/twitter/user-tweets/processed']
|
||||
with SevenZipFile(packed_data, 'w') as z:
|
||||
z: SevenZipFile = z
|
||||
for p in processed_dirs:
|
||||
debug(f'- Packing {p}')
|
||||
z.writeall(DATA_DIR + p)
|
||||
|
||||
# Pack resources
|
||||
debug('Packing resources...')
|
||||
with SevenZipFile(packed_res, 'w') as z:
|
||||
z: SevenZipFile = z
|
||||
z.writeall(RES_DIR)
|
||||
|
||||
# Pack MarkUs submission
|
||||
# Even though 7zip has much better compression rate than zip, MarkUs only supports zip.
|
||||
debug('Packing source code...')
|
||||
with zipfile.ZipFile(f'{packed_dir}/markus.zip', 'w') as zf:
|
||||
z: zipfile.ZipFile = zf
|
||||
|
||||
# Add sources
|
||||
src_path = Path(os.path.realpath(__file__)).parent
|
||||
for f in os.listdir(src_path):
|
||||
if not os.path.isdir(f) and f != '.DS_Store' and not f.startswith('._'):
|
||||
z.write(f)
|
||||
|
||||
# Add packed resource
|
||||
z.write(packed_res, 'resources.7z')
|
||||
|
||||
# Add report tex
|
||||
for file in ['project_report.tex', 'project_report.pdf']:
|
||||
z.write(os.path.join(src_path, f'../writing/report/{file}'), file)
|
||||
|
||||
# Open packed location (Since there isn't a platform-independent way of doing this, we currently
|
||||
# only support macOS)
|
||||
if sys.platform == 'darwin':
|
||||
os.system(f'open {Path(packed_dir).absolute()}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
python_ta.check_all(config={
|
||||
'extra-imports': ['json', 'os', 'random', 'sys', 'zipfile', 'dataclasses', 'datetime',
|
||||
'pathlib', 'typing', 'requests', 'bs4', 'py7zr', 'constants', 'utils'
|
||||
], # the names (strs) of imported modules
|
||||
'allowed-io': [], # the names (strs) of functions that call print/open/input
|
||||
'max-line-length': 100,
|
||||
'disable': ['R1705', 'C0200']
|
||||
}, output='pyta_report.html')
|
||||
-160
@@ -1,160 +0,0 @@
|
||||
"""CSC110 Fall 2021 Project
|
||||
This module generates report HTML and serves it in an HTTP server.
|
||||
"""
|
||||
|
||||
import json
|
||||
import os.path
|
||||
import shutil
|
||||
import traceback
|
||||
import webbrowser
|
||||
from distutils.dir_util import copy_tree
|
||||
from pathlib import Path
|
||||
|
||||
from flask import Flask, send_from_directory, Response
|
||||
|
||||
import python_ta
|
||||
import python_ta.contracts
|
||||
|
||||
from constants import REPORT_DIR, DEBUG, RES_DIR
|
||||
from utils import read, write
|
||||
|
||||
|
||||
def generate_report() -> str:
|
||||
"""
|
||||
Compile the report document and generate a markdown report
|
||||
|
||||
Preconditions:
|
||||
- RES_DIR exists, and contains the necessary resources used in this project.
|
||||
|
||||
:return: Markdown report
|
||||
"""
|
||||
# Load markdown
|
||||
md = read(os.path.join(RES_DIR, './report_document.md')).replace('\r\n', '\n').split('\n')
|
||||
|
||||
# Process line by line
|
||||
for i in range(len(md)):
|
||||
line = md[i]
|
||||
if not line.startswith('@include'):
|
||||
continue
|
||||
|
||||
# Process @include statements
|
||||
# noinspection PyBroadException
|
||||
try:
|
||||
path = line[line.index('`') + 1:]
|
||||
path = path[:path.index('`')]
|
||||
md[i] = read(REPORT_DIR + path)
|
||||
|
||||
# Cut lines
|
||||
# Format: @include-cut `path` <start, inclusive> [end, not inclusive]
|
||||
if line.startswith('@include-cut'):
|
||||
args = [int(j) for j in line.split()[2:]]
|
||||
if len(args) == 1:
|
||||
md[i] = '\n'.join(md[i].split('\n')[args[0]:])
|
||||
if len(args) == 2:
|
||||
md[i] = '\n'.join(md[i].split('\n')[args[0]:args[1]])
|
||||
|
||||
# Specific lines
|
||||
# Format: @include-lines `path` <...lines>
|
||||
# Example: @include-lines `path` 1 2 5
|
||||
if line.startswith('@include-lines'):
|
||||
args = [int(j) for j in line.split()[2:]]
|
||||
lines = md[i].split('\n')
|
||||
lines = [lines[ln] for ln in range(len(lines)) if ln in args]
|
||||
md[i] = '\n'.join(lines)
|
||||
|
||||
# Handle errors. (It prompts "too broad an exception clause" but I actually need to catch
|
||||
# every possible exception.)
|
||||
except Exception:
|
||||
md[i] = f"<pre class=\"error\">" \
|
||||
f"\nInvalid @include statement. \n{traceback.format_exc()}</pre>"
|
||||
|
||||
return '\n'.join(md)
|
||||
|
||||
|
||||
def generate_html() -> str:
|
||||
"""
|
||||
Generate report then put it into the HTML template
|
||||
|
||||
:return: HTML string
|
||||
"""
|
||||
# Generate markdown report and JSON encode it (which works as JS code! amazing)
|
||||
md_json = json.dumps({'content': generate_report()})
|
||||
# Inject into HTML
|
||||
html = read(os.path.join(RES_DIR, 'report_page.html')) \
|
||||
.replace('`{{markdown}}`', md_json)
|
||||
return html
|
||||
|
||||
|
||||
def write_html() -> None:
|
||||
"""
|
||||
Write HTML and copy files to ./dist
|
||||
|
||||
:return: None
|
||||
"""
|
||||
if os.path.isdir('./dist'):
|
||||
shutil.rmtree('./dist')
|
||||
Path('./dist/html').mkdir(parents=True, exist_ok=True)
|
||||
write('./dist/index.html', generate_html())
|
||||
|
||||
copy_tree(os.path.join(RES_DIR, 'html/'), './dist/html')
|
||||
copy_tree(REPORT_DIR, './dist')
|
||||
|
||||
|
||||
def serve_report() -> None:
|
||||
"""
|
||||
Serve report page in an http server.
|
||||
|
||||
:return: None
|
||||
"""
|
||||
# Create flask app
|
||||
app = Flask(__name__)
|
||||
html = generate_html()
|
||||
|
||||
@app.route('/')
|
||||
def root() -> str:
|
||||
"""
|
||||
Root webpage. If debug mode is enabled, generate new HTML every time the web page is
|
||||
accessed. Else, serve the generated HTML.
|
||||
|
||||
:return: HTML report
|
||||
"""
|
||||
if DEBUG:
|
||||
return generate_html()
|
||||
else:
|
||||
return html
|
||||
|
||||
@app.route('/<path:path>')
|
||||
def res(path: str) -> Response:
|
||||
"""
|
||||
Resources endpoint. This function maps report queries to the report directory
|
||||
|
||||
:param path: Path of the resource
|
||||
:return: File resource or 404
|
||||
"""
|
||||
return send_from_directory(Path(REPORT_DIR).absolute(), path)
|
||||
|
||||
@app.route('/html/<path:path>')
|
||||
def js_res(path: str) -> Response:
|
||||
"""
|
||||
JS Resource endpoint. This maps JS and CSS queries to the resources directory
|
||||
|
||||
:param path: Path of the resource
|
||||
:return: File resource or 404
|
||||
"""
|
||||
return send_from_directory(os.path.join(RES_DIR, 'html'), path)
|
||||
|
||||
# Run app
|
||||
webbrowser.open("http://localhost:8080")
|
||||
app.run(port=8080)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
python_ta.contracts.check_all_contracts()
|
||||
python_ta.check_all(config={
|
||||
'extra-imports': ['json', 'os.path', 'shutil', 'traceback', 'webbrowser',
|
||||
'distutils.dir_util', 'pathlib', 'flask', 'constants', 'utils'
|
||||
], # the names (strs) of imported modules
|
||||
'allowed-io': [], # the names (strs) of functions that call print/open/input
|
||||
'max-line-length': 100,
|
||||
'disable': ['R1705', 'C0200', 'R1702', 'W0703']
|
||||
})
|
||||
Binary file not shown.
@@ -1,208 +0,0 @@
|
||||
|
||||
# Shifting Interest in COVID-19 Twitter Posts
|
||||
|
||||
## Introduction
|
||||
|
||||
We have observed that there have been increasingly more voices talking about COVID-19 since the start of the pandemic. However, different groups of people might view the importance of discussing the pandemic differently. For example, we don't know whether the most popular people on Twitter will be more or less inclined to post COVID-related content than the average Twitter user. Also, while some audience finds these content interesting, others quickly scroll through them. **So, we aim to compare people's interests in posting coronavirus content and the audience's interests in viewing them between different groups.** Also, with recent developments and policy changes toward COVID-19, it is unclear how people's discussions would react. Some people might believe that the pandemic is starting to end so that discussing it would seem increasingly like an unnecessary effort, while others might find these policy changes controversial and want to voice their opinions even more. Also, even though COVID-related topics are almost always on the news, some news outlets might intentionally cover them more frequently than others. For the people watching the news, some people might find these news reports interesting, while others can't help but switch channels. So, how people's interest in listening or discussing COVID-related topics changes over time is not very clear. **Our second goal is to analyze how people's interest in COVID-related topics changes and how frequently people have discussed COVID-related issues in the two years since the pandemic started.**
|
||||
|
||||
# Method
|
||||
|
||||
## Demographics
|
||||
|
||||
Our data come from three samples:
|
||||
|
||||
* `500-pop`: The list of 500 most followed users on Twitter who post in English, Chinese, or Japanese.
|
||||
* `500-rand`: A sample of 500 random users on Twitter who post in English, Chinese, or Japanese with at least 1000 posts and at least 150 followers.
|
||||
* `eng-news`: A list of 100 top news Twitter accounts by Nur Bremmen [[1]](#ref1), combined with all news accounts which TwitterNews reposted. All of them post in English, and most of them target audience in North America.
|
||||
|
||||
We also counted the number of people speaking each language:
|
||||
|
||||
@include `/sample-demographics.md`
|
||||
|
||||
## Data Collection
|
||||
|
||||
1. To create our samples, we collected a wide range of Twitter users using Twitter's get friends list API endpoint [(documentation)](https://developer.twitter.com/en/docs/twitter-api/v1/accounts-and-users/follow-search-get-users/api-reference/get-friends-list) and the follows-chaining technique. We specified one single user as the starting point, obtained the user's friends list, then we picked 3 random users and 3 most followed users from the friend list, add them to the queue, and start the process again from each of them. Because of Twitter's rate limiting on the get friends list endpoint, we can only obtain a maximum of 200 users per minute, with many of them being duplicates. We ran the program for one day and obtained 224,619 users (852.3 MB decompressed). However, only the username, popularity, post count, and language data are kept after processing (filtering). The processed user dataset `data/twitter/user/processed/users.json` is 7.9 MB in total. We selected our samples by filtering the results first based on language, selected the top 500 most followed users as `500-pop`, filtered the list again based on post count (>1000) and followers (>150), then selected a random sample of 500 users as `500-rand`.
|
||||
|
||||
2. We also downloaded all tweets from our sampled users through the user-timeline API [(documentation)](https://developer.twitter.com/en/docs/twitter-api/v1/tweets/timelines/api-reference/get-statuses-user_timeline). Due to rate limiting, the program took around 16 hours to finish, and we obtained 7.7 GB of raw data (uncompressed). During processing, for each tweet, we extracted only its date, popularity (likes + retweets), whether it is a retweet, and whether it is COVID-related. The text of the tweets are not retained, and the processed data directory `data/twitter/user-tweets/processed/` is 141.6 MB in total.
|
||||
|
||||
3. We also used the COVID-19 daily cases data published by New York Times [[3]](#ref3) to compare with peaks and through in our frequency over date graph.
|
||||
|
||||
## Computation & Filtering
|
||||
|
||||
To analyze the frequencies and relative popularity of COVID-related posting either for all posts from a specific user, or for a sample across many users for a specific date, we defined several formulas. First, we need to define many terms we will use in the following sections:
|
||||
|
||||
* **Frequency**: The percentage of COVID-related posts compared to all posts, showing how frequent COVID-related content are posted.
|
||||
* **Popularity**: The integer value representing the popularity of a post, measured by the total number of user interactions on a post, which is the number of likes and comments on a tweet combined.
|
||||
* **Popularity Ratio**: The relative popularity between 0 and infinity calculating how popular are a user's COVID-posts compared to all the user's posts, which is a ratio of the average popularity of COVID-posts over all posts. If COVID-posts are more popular, then this value should be greater than 1, and if they are less popular, this value should be less than 1. Since follower count and interaction rate differs wildly between users, we cannot assume that popularity is comparable between users, so popularity is only compared within a user, while popularity ratio can be compared across users.
|
||||
|
||||
### 1. Computation - User Analysis
|
||||
|
||||
In the first section, we used the following formulas to calculate statistical distributions of the frequencies and popularity ratios of users in a sample:
|
||||
|
||||
<blockquote>
|
||||
$$ \text{freq}_{u} = \frac{|\text{COVID-posts by } u|}{|\text{All posts by } u|} $$
|
||||
</blockquote>
|
||||
|
||||
<blockquote>
|
||||
$$ \text{pop_ratio}_{u} = \left(\frac{\sum\text{Popularity of COVID-posts by } u}{|\text{COVID-posts by } u|}\right) / \left(\frac{\sum \text{Popularity of all posts by } u}{|\text{All posts by } u|}\right) $$
|
||||
</blockquote>
|
||||
|
||||
The frequency equation can divide by zero if the user has zero posts, and it is logical to assign the frequency to 0 when the user didn't post anything. However, it is not sensible to assign the popularity ratio to zero when the pop_ratio equation divides by zero. There are three divisions in the pop_ratio equation, so there are three possible places where it might divide by zero. To prevent division by zero, people who didn't post about COVID-19, who didn't post anything at all, and who have literally 0 popularity on any of their posts are ignored. In our data, this amount of people are ignored for each sample:
|
||||
|
||||
@include `/pop/ignored.md`
|
||||
|
||||
Then, the users' results are graphed in one histogram for each sample to gain some insights about the distribution of user frequencies. However, there are many outliers and more than half who posted below 0.1% for two of our samples, making the graphs unreadable: (You can click on the images to enlarge them, and hold down E to view full screen)
|
||||
|
||||
<div class="image-row">
|
||||
<div><img src="/freq/500-pop-hist-outliers.png" alt="hist"></div>
|
||||
<div><img src="/freq/500-rand-hist-outliers.png" alt="hist"></div>
|
||||
<div><img src="/freq/eng-news-hist-outliers.png" alt="hist"></div>
|
||||
</div>
|
||||
|
||||
For example, even though most of `500-rand` are concentrated below 10%, the x-axis scale is stretched to 50% by many outliers who post more than 40%:
|
||||
|
||||
@include-cut `/freq/500-rand-top-20.md` 0 8
|
||||
|
||||
To resolve this, the outliers are removed both for frequencies and popularity ratios using the method proposed by Boris Iglewicz and David Hoaglin (1993) [[2]](#ref2), and for frequencies, everyone who posted below 0.1% are ignored when graphing histograms. They are not ignored in statistic calculations.
|
||||
|
||||
### 2. Computation - Change Analysis
|
||||
|
||||
The second section analyzes data separate for each of our samples, just like the first section. However, unlike how calculations are separated for each user in the first section, the second section separates calculation by date and combines users in a sample. We defined the start of COVID-19 as _2020-01-01_ and ignored all posts prior to this date. Then, the average frequency and popularity ratio are calculated for every day since _2020-01-01_. This calculation gave us a list `freqs` and a list `pops` where, for every date `dates[i]`,
|
||||
|
||||
<blockquote>
|
||||
$$ \text{freq}_i = \frac{|\text{COVID-posts on date}_{i}|}{|\text{All posts on date}_{i}|} $$
|
||||
</blockquote>
|
||||
|
||||
<blockquote>
|
||||
$$ \text{pop_ratio}_i = \frac{ \sum_{u \in \text{Users}} \left(\frac{\sum\text{Popularity of u's COVID-posts on date}_i}{|\text{u's COVID-posts on date}_i| \cdot (\text{Average popularity of all u's posts})}\right)}{(\text{Number of users posted on date}_i)} $$
|
||||
</blockquote>
|
||||
|
||||
After calculation, `freqs` and `pops` are plotted in line graphs against `dates`. Initially, we are seeing graphs with very high peaks such as the graph below. After some investigation, we found that these peaks are caused by not having enough tweets on each day to average out the random error of one single popular tweet. For example, in the graph below, we adjusted the program to print different users' popularity ratios when we found an average popularity ratio of greater than 20, which produced the output on the right. As it turns out, on 2020-07-11, the user @juniorbachchan posted that he and his father tested positive, and that single post is 163.84 times more popular than the average of all his posts. (The post is linked [here](https://twitter.com/juniorbachchan/status/1282018653215395840), it has 235k likes, 25k comments, and 32k retweets). Even though these data points are outliers, there isn't an effective way of removing them since we don't have enough tweets data from each user to calculate their range (for example, someone's COVID-related post might be the only one they've posted). So, we've decided to limit the viewing window to `y = [0, 2]` as shown in the graph on the right.
|
||||
|
||||
<div class="image-row">
|
||||
<div><img src="html/peak-1.png" alt="graph"></div>
|
||||
<div style="display: flex; flex-direction: column; justify-content: center"><pre>
|
||||
Date: 2020-07-11
|
||||
- JoeBiden 1.36
|
||||
<span class="highlight">- juniorbachchan 163.84</span>
|
||||
- victoriabeckham 0.80
|
||||
- anandmahindra 7.66
|
||||
- gucci 0.13
|
||||
- StephenKing 0.61
|
||||
</pre></div>
|
||||
<div><img src="html/peak-2.png" alt="graph"></div>
|
||||
</div>
|
||||
|
||||
Then, we encountered the issue of noise. When we plot the graph without a filter, we found that the graph is actually very noisy. We decided to average the results over 7 days. Then, we also experimented with different filters from the `scipy` library and different parameter values, and chose to use an IIR filter with `n = 10`.
|
||||
|
||||
<div class="image-row">
|
||||
<div><img src="/change/n/5.png" alt="graph"></div>
|
||||
<div><img src="/change/n/10.png" alt="graph"></div>
|
||||
<div><img src="/change/n/15.png" alt="graph"></div>
|
||||
</div>
|
||||
|
||||
# Results
|
||||
|
||||
## User Analysis
|
||||
|
||||
This section ignores date and focuses on user differences within our samples, which will answer the first part of our research question: **how frequently does people post about COVID-related issues, and how interested are people to see COVID-related posts?**
|
||||
|
||||
### 1. User Posting Frequency
|
||||
|
||||
First, the users' COVID-related posting frequency in these three datasets are analyzed. Initially, we were expecting that most people will post coronavirus content because this pandemic is very relevant to everyone. However, there are many people in our samples didn't post about COVID-19 at all. The following table shows how many people in each sample didn't post or posted less than 1% about COVID-19:
|
||||
|
||||
@include `/freq/didnt-post.md`
|
||||
|
||||
The `eng-news` sample has the lowest number of users who didn't have COVID-related posts, the `500-rand` sample has the highest, while `500-pop` sits in between. This large difference between `eng-news` and the rest can be explained by the news channels' obligation to report news, which includes news about new outbreaks, progress of vaccination, new cross-border policies, etc. Also, `500-pop` has much more users who posted COVID-related content than `500-rand`, while they have similar amounts of users posting less than 1%. This finding might be explained by how influential people have more incentive to express their support toward slowing the spread of the pandemic than regular users, which doesn't require frequent posting like news channels.
|
||||
|
||||
Then, the calculated frequency data for each user in a sample are graphed in histograms:
|
||||
|
||||
<div class="image-row">
|
||||
<div><img src="/freq/500-pop-hist.png" alt="hist"></div>
|
||||
<div><img src="/freq/500-rand-hist.png" alt="hist"></div>
|
||||
<div><img src="/freq/eng-news-hist.png" alt="hist"></div>
|
||||
</div>
|
||||
|
||||
As expected, the distributions looks right-skewed, with most people posting not very much. One interesting distinction is that, even though the distributions follow similar shapes, the x-axis ticks of `eng-news` is actually ten times larger than the other two, which means that `eng-news` post a lot more about COVID-19 on average than the other two samples. Statistics of the samples are calculated to further verify these insights:
|
||||
|
||||
@include-lines `/freq/stats.md` 0 1 4 5 6 7
|
||||
|
||||
Since there are many outliers, medians and IQR will more accurately represent the center and spread of this distribution. As these numbers show, `eng-news` do post much more (a 6.1% increment in post frequency, or a 406.7% increase) than the other two samples. Again, this can be explained by the news channels' obligation to report news related to COVID-19 or to promote methods to slow the spread of the pandemic. These means also shows that 50% of average Twitter users dedicate below 1.5% of their timeline to COVID-related posts.
|
||||
|
||||
### 2. User Popularity Ratios
|
||||
|
||||
Similar histograms are graphed and statistics are calculated for user's popularity ratios in their sample, calculated using the formula described in the methods section:
|
||||
|
||||
<div class="image-row">
|
||||
<div><img src="/pop/500-pop-hist.png" alt="hist"></div>
|
||||
<div><img src="/pop/500-rand-hist.png" alt="hist"></div>
|
||||
<div><img src="/pop/eng-news-hist.png" alt="hist"></div>
|
||||
</div>
|
||||
|
||||
Looking at the histograms, while `eng-news` is roughly symmetric, the other two distributions are right skewed.
|
||||
|
||||
@include-lines `/pop/stats.md` 0 1 4 5 6 7
|
||||
|
||||
The calculated medians show that the audience normally don't like or comment on COVID-related posts as much as other posts by all three groups, which implies that people aren't as interested in these posts. The average Twitter user's and the average English news channel's COVID-posts has only 87% of the popularity compared to their other posts, while the average `500-pop` user has only 69% of the popularity. This difference is possibly because the most popular users' audience probably followed them for the specific types of content that only they can post, and not general COVID-related content that anyone can post similarly.
|
||||
|
||||
Also, even though the medians for `500-rand` and `eng-news` are the same, since the `500-rand` distribution is right skewed, its 25th percentile is much lower—25% of average Twitter users' COVID-posts are only 34% as popular as their other posts.
|
||||
|
||||
## Change Analysis
|
||||
|
||||
After we answered how frequently people posted about COVID-19 and how interested are people to view these posts, we analyze our data over the posting dates to answer the second part of our research question: **How does posting frequency and people's interests in COVID-19 posts changes from the beginning of the pandemic to now?**
|
||||
|
||||
### 1. Posting Frequency Over Time
|
||||
|
||||
We graphed the posting frequencies of our three samples in line graphs with the x-axis being the date with labels representing the month, which gave us the following graphs:
|
||||
|
||||
<div class="image-row">
|
||||
<div><img src="/change/freq/500-pop.png" alt="graph"></div>
|
||||
<div><img src="/change/freq/500-rand.png" alt="graph"></div>
|
||||
<div><img src="/change/freq/eng-news.png" alt="graph"></div>
|
||||
</div>
|
||||
|
||||
Looking at three graphs individually, the posting rates were almost zero during the first two month when COVID-19 first started for all three samples, which is expected because no one knew how devastating it will be at that time. Then, all three samples had a peak in posting frequencies from March to June 2020. After June 2020, the posting rate for both `500-rand` and `eng-news` declined to around 1/3 of the peak, with `500-pop` declining slightly as well. While the reason to this decline is unclear, we speculate that it might be caused by people's loss of interest in the topic as they realize COVID-19 isn't going to be a disaster that fades away quickly, or as the news became less "breaking" and information started to repeat. Like the selective attention theory of cognitive psychology, people's attention to one thing comes at the expense of others since our attention is very limited. So people might have chosen to direct more attention to living rather than paying attention to the coronavirus that didn't seem to go away soon. Also, similar to how people will unconsciously learn to ignore repeated background noise after moving to a new environment (in a process called habituation), they might have learned to ignore the repeated information about COVID-19, which will lead to less COVID-related posting. Further research can determine whether this three-month attention span can generalize to other long-term disaster other than COVID-19.
|
||||
|
||||
After June 2020, `500-rand` continued declining steadily without major peaks, while `eng-news` had a smaller peak around Dec 2020 and a trough after June 2021, and `500-top` had many peaks and toughs after. In an effort to interpret these peaks, we overlapped the three charts with the data of new COVID-19 cases in the U.S. published by New York Times [[3]](#ref3), which gave us the following graph:
|
||||
|
||||
<div class="image-row">
|
||||
<div><img src="/change/comb/freq.png" alt="graph" class="large"></div>
|
||||
</div>
|
||||
|
||||
In this graph, we can see that the peak around Dec 2020 and the trough around Jun 2021 in `eng-news` and `500-pop` actually correspond very closely with the rise and fall of new cases in the U.S., which is reasonable because there are more sensational news to report and more COVID-related events happening to popular individuals when cases are high. However, even though the first peak in cases around August 2020 did correlate with a peak in `500-rand`, the rise and fall of cases in the U.S. doesn't seem to affect `500-rand` overall. This is possibly because we included three languages in the population of our random sample, which means that `500-rand` isn't limited to English-speaking accounts that mostly target the U.S. audience like `eng-news`.
|
||||
|
||||
### 2. Popularity Ratio Over Time
|
||||
|
||||
We graphed a similar graph with popularity ratio being the y-axis over date as the x-axis, as shown below:
|
||||
|
||||
<div class="image-row">
|
||||
<div><img src="/change/comb/pop.png" alt="graph" class="large"></div>
|
||||
</div>
|
||||
|
||||
Despite efforts to filter out noise or normalize the graph discussed in the [method](#method) section, we did not find any patterns in the resulting graph. The peaks and troughs of each line seems random, and the three lines did not have common peaks or troughs that might reveal meaningful insights. The raw data looks very much like random noise as well. This lack of meaningful information is possibly because our sample size is comparatively small—even though we have 500 users in our `500-pop` sample, the sample size for tweets by these users on one specific day is very small. For example, there are only 6 users in `500-pop` who posted on `2020-07-11`. This lack of samples amplified the effect of randomness, and more data may be needed to reduce the effect of one tweet on the popularity ratio for the specified date. Unfortunately, we have to reach a conclusion that more data is needed to reveal interesting findings.
|
||||
|
||||
# Conclusion
|
||||
|
||||
In summary, key findings in our research include that while news channels post about COVID-19 more frequently (Median = 7.6%), average Twitter users and most popular users don't post very much (Median ≤ 1.5%). And while COVID-posting frequencies for `eng-news` and `500-pop` fluctuates with the number of new cases in the U.S., average Twitter users' COVID-posting frequency dropped and continued to decrease since Jun 2020. And these posts were not as popular (not liked or commented as much) as users' other posts (Median ≤ 0.87).
|
||||
|
||||
These findings might not be surprising, but they might have again demonstrated people's ability to adapt to new environments. The duration of the sensational effect of the start of COVID-19 might be similar to the grief from losing something important, they all fade over time as we adapt to them. Even though people focused a lot of attention on COVID-19 when new information first became available from March 2020, people's interest in these topics decreased as we adapt to the new norm with COVID-19 in three months, demonstrated by the quickly decreasing posting rates. Or, for the audience, rather than liking or commenting on COVID-19 posts, they might have quickly scrolled through them in favor of more interesting posts. It is fascinating that we can learn to adapt to such a devastating change in our environment in only three months.
|
||||
|
||||
# References
|
||||
|
||||
<a id="ref1"></a>
|
||||
|
||||
[1] Bremmen, N. (2010, September 3). The 100 most influential news media Twitter accounts. _Memeburn_. Retrieved November 27, 2021, from https://memeburn.com/2010/09/the-100-most-influential-news-media-twitter-accounts/.
|
||||
|
||||
<a id="ref2"></a>
|
||||
|
||||
[2] Iglewicz, Boris, & David Hoaglin (1993), "Volume 16: How to Detect and
|
||||
Handle Outliers", _The ASQC Basic References in Quality Control:
|
||||
Statistical Techniques_, Edward F. Mykytka, Ph.D., Editor.
|
||||
|
||||
<a id="ref3"></a>
|
||||
|
||||
[3] The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved November 27, 2021, from https://github.com/nytimes/covid-19-data.
|
||||
|
||||
<a id="ref4"></a>
|
||||
|
||||
[4] WHO. (n.d.) _Listings of WHO's Response to COVID-19._ World Health Organization. Retrieved November 27, 2021, from https://www.who.int/news/item/29-06-2020-covidtimeline.
|
||||
@@ -1,63 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>CSC110 Report</title>
|
||||
<link rel="stylesheet" href="html/style.css">
|
||||
</head>
|
||||
<body>
|
||||
<div id="content">
|
||||
|
||||
</div>
|
||||
<script src="html/marked.min.js"></script>
|
||||
<script src="html/jquery.min.js"></script>
|
||||
<script src="html/polyfill.es6.min.js"></script>
|
||||
<script src="html/mathjax-tex-mml-chtml.js"></script>
|
||||
|
||||
<script>
|
||||
|
||||
// Python will inject the markdown code here.
|
||||
markdown = `{{markdown}}`
|
||||
|
||||
document.getElementById('content').innerHTML =
|
||||
marked.parse(markdown.content);
|
||||
|
||||
// Make images clickable
|
||||
// Improved from: https://stackoverflow.com/a/50430187/7346633
|
||||
body = $('body')
|
||||
$('img').addClass('clickable').click(function() {
|
||||
const src = $(this).attr('src');
|
||||
let modal;
|
||||
|
||||
function removeModal() {
|
||||
modal.remove();
|
||||
body.off('keyup.modal-close');
|
||||
}
|
||||
|
||||
modal = $('<div id="modal">').css({
|
||||
background: 'RGBA(0,0,0,.5) url(' + src + ') no-repeat center',
|
||||
backgroundSize: $(this).hasClass('large') ? 'contain' : 'auto',
|
||||
width: '100vw',
|
||||
height: '100vh',
|
||||
position: 'fixed',
|
||||
zIndex: '100',
|
||||
top: '0',
|
||||
left: '0',
|
||||
cursor: 'zoom-out'
|
||||
}).click(function() {
|
||||
removeModal();
|
||||
}).appendTo('body');
|
||||
|
||||
// Handling keyboard shortcuts
|
||||
body.on('keyup.modal-close', (e) => {
|
||||
if (e.key === 'Escape') removeModal();
|
||||
if (e.key === 'e') modal.removeClass('zoom')
|
||||
});
|
||||
body.on('keydown.modal-close', (e) => {
|
||||
if (e.key === 'e') modal.addClass('zoom')
|
||||
})
|
||||
});
|
||||
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
-460
@@ -1,460 +0,0 @@
|
||||
"""CSC110 Fall 2021 Project
|
||||
This module contains useful functions and classes, including:
|
||||
- debug messages
|
||||
- file I/O
|
||||
- statistics functions, removing outliers and averaging values over a period
|
||||
- date-related functions
|
||||
- classes for configs, reports, statistics, and JSON
|
||||
"""
|
||||
|
||||
import dataclasses
|
||||
import doctest
|
||||
import inspect
|
||||
import json
|
||||
import os
|
||||
import statistics
|
||||
import math # python_ta complains about unused import but it's used in a doctest
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime, date, timedelta
|
||||
from pathlib import Path
|
||||
from typing import Union, Any, Generator
|
||||
|
||||
import json5
|
||||
import numpy as np
|
||||
import python_ta
|
||||
import python_ta.contracts
|
||||
from tabulate import tabulate
|
||||
|
||||
from constants import REPORT_DIR, DEBUG
|
||||
|
||||
|
||||
@dataclass
|
||||
class Config:
|
||||
"""
|
||||
Secrets configuration for this program.
|
||||
|
||||
Attributes:
|
||||
- consumer_key: The consumer key from the Twitter application portal
|
||||
- consumer_secret: The consumer secret from the Twitter application portal
|
||||
- access_token: The access token of an app from the Twitter application portal
|
||||
- access_secret: The access secret of an app from the Twitter application portal
|
||||
|
||||
Representation Invariants:
|
||||
- self.consumer_key != ''
|
||||
- self.consumer_secret != ''
|
||||
- self.access_token != ''
|
||||
- self.access_secret != ''
|
||||
"""
|
||||
# Twitter's official API v1 keys
|
||||
consumer_key: str
|
||||
consumer_secret: str
|
||||
access_token: str
|
||||
access_secret: str
|
||||
|
||||
|
||||
def load_config(path: str = 'config.json5') -> Config:
|
||||
"""
|
||||
Load config using JSON5, from either the local file ~/config.json5 or from the environment
|
||||
variable named config.
|
||||
|
||||
:param path: Path of the config file (Default: config.json5)
|
||||
:return: Config object
|
||||
"""
|
||||
if os.path.isfile(path):
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
conf = json5.load(f)
|
||||
else:
|
||||
conf = json5.loads(os.getenv('config'))
|
||||
|
||||
return Config(**conf)
|
||||
|
||||
|
||||
def debug(msg: object) -> None:
|
||||
"""
|
||||
Output a debug message, usually from another function
|
||||
|
||||
:param msg: Message
|
||||
"""
|
||||
if DEBUG:
|
||||
caller = inspect.stack()[1].function
|
||||
print(f'[DEBUG] {caller}: {msg}')
|
||||
|
||||
|
||||
def calculate_rate_delay(rate_limit: float) -> float:
|
||||
"""
|
||||
Calculate the rate delay for each request given rate limit in request per minute
|
||||
|
||||
:param rate_limit: Rate limit in requests per minute
|
||||
:return: Rate delay in seconds per request
|
||||
"""
|
||||
return 1 / rate_limit * 60
|
||||
|
||||
|
||||
def write(file: str, text: str) -> None:
|
||||
"""
|
||||
Write text to a file
|
||||
|
||||
Preconditions:
|
||||
- file != ''
|
||||
|
||||
:param file: File path (will be converted to lowercase)
|
||||
:param text: Text
|
||||
:return: None
|
||||
"""
|
||||
file = file.lower().replace('\\', '/')
|
||||
|
||||
if '/' in file:
|
||||
Path(file).parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
with open(file, 'w', encoding='utf-8') as f:
|
||||
f.write(text)
|
||||
|
||||
|
||||
def read(file: str) -> str:
|
||||
"""
|
||||
Read file content
|
||||
|
||||
Preconditions:
|
||||
- file != ''
|
||||
|
||||
:param file: File path (will be converted to lowercase)
|
||||
:return: None
|
||||
"""
|
||||
with open(file.lower(), 'r', encoding='utf-8') as f:
|
||||
return f.read()
|
||||
|
||||
|
||||
class Reporter:
|
||||
"""
|
||||
Report file creator
|
||||
|
||||
Attributes:
|
||||
- report: The string of the report
|
||||
- file: Where the report is stored
|
||||
|
||||
Representation Invariants:
|
||||
- self.file != ''
|
||||
"""
|
||||
report: str
|
||||
file: str
|
||||
|
||||
def __init__(self, file: str) -> None:
|
||||
self.report = ''
|
||||
self.file = os.path.join(REPORT_DIR, file)
|
||||
|
||||
def print(self, line: str = '', arg: Any = None, autosave: bool = True) -> None:
|
||||
"""
|
||||
Add a line to the report
|
||||
|
||||
:param line: Line content
|
||||
:param arg: Additional argument
|
||||
:param autosave: Save automatically
|
||||
:return: self (this is for call chaining, this way you can call Reporter.print().save()
|
||||
"""
|
||||
self.report += line
|
||||
if arg is not None:
|
||||
self.report += str(arg)
|
||||
self.report += '\n'
|
||||
if autosave:
|
||||
self.save()
|
||||
|
||||
def save(self) -> None:
|
||||
"""
|
||||
Save the report to the file
|
||||
|
||||
:return: None
|
||||
"""
|
||||
write(self.file, self.report)
|
||||
|
||||
def table(self, table: list[list[str]], headers: list[str], header_code: bool = False) -> None:
|
||||
"""
|
||||
Report a table
|
||||
|
||||
:param table: Table data
|
||||
:param headers: Headers
|
||||
:param header_code: Whether the headers should be code-formatted
|
||||
:return: None
|
||||
"""
|
||||
if header_code:
|
||||
headers = [f'`{s}`' for s in headers]
|
||||
self.print(tabulate(table, headers, tablefmt='github'))
|
||||
|
||||
|
||||
def remove_outliers(points: list[float], z_threshold: float = 3.5) -> list[float]:
|
||||
"""
|
||||
Create list with outliers removed for graphing
|
||||
|
||||
Credit to: https://stackoverflow.com/a/11886564/7346633
|
||||
|
||||
Preconditions:
|
||||
- len(points) > 0
|
||||
|
||||
:param points: Input points list
|
||||
:param z_threshold: Z threshold for identifying whether a point is an outlier
|
||||
:return: List with outliers removed
|
||||
"""
|
||||
x = np.array(points)
|
||||
if len(x.shape) == 1:
|
||||
x = x[:, None]
|
||||
median = np.median(x, axis=0)
|
||||
diff = np.sum((x - median) ** 2, axis=-1)
|
||||
diff = np.sqrt(diff)
|
||||
med_abs_deviation = np.median(diff)
|
||||
|
||||
modified_z_score = 0.6745 * diff / med_abs_deviation
|
||||
|
||||
is_outlier = modified_z_score > z_threshold
|
||||
|
||||
return [points[v] for v in range(len(x)) if not is_outlier[v]]
|
||||
|
||||
|
||||
@dataclass()
|
||||
class Stats:
|
||||
"""
|
||||
Data class storing the statistics of a sample
|
||||
|
||||
Attributes:
|
||||
- mean: The average of the sample
|
||||
- stddev: The standard deviation
|
||||
- median: The median value of the sample, or the 50th percentile
|
||||
- iqr: The interquartile-range (75th percentile - 25th percentile)
|
||||
- q25: The first quartile, or the 25th percentile
|
||||
- q75: The third quartile, or the 75th percentile
|
||||
"""
|
||||
mean: float
|
||||
stddev: float
|
||||
median: float
|
||||
iqr: float
|
||||
q25: float
|
||||
q75: float
|
||||
|
||||
|
||||
def get_statistics(points: list[float]) -> Stats:
|
||||
"""
|
||||
Calculate statistics for a set of points
|
||||
|
||||
Preconditions:
|
||||
- len(points) > 0
|
||||
|
||||
:param points: Input points
|
||||
:return: Statistics
|
||||
"""
|
||||
q75, q25 = np.percentile(points, [75, 25])
|
||||
iqr = q75 - q25
|
||||
return Stats(statistics.mean(points), statistics.stdev(points), statistics.median(points),
|
||||
iqr, q25, q75)
|
||||
|
||||
|
||||
def tabulate_stats(stats: list[Stats], percent: bool = False) -> list[list[str]]:
|
||||
"""
|
||||
Create a table structure from statistics for tabulate
|
||||
|
||||
:param stats: Statistics
|
||||
:param percent: Whether the numbers are percentages
|
||||
:return: Table for tabulate
|
||||
"""
|
||||
|
||||
def num(n: float) -> str:
|
||||
return f'{n:.2f}' if not percent else f'{n * 100:.1f}%'
|
||||
|
||||
return [['Mean'] + [num(s.mean) for s in stats],
|
||||
['StdDev'] + [num(s.stddev) for s in stats],
|
||||
['Median'] + [num(s.median) for s in stats],
|
||||
['IQR'] + [num(s.iqr) for s in stats],
|
||||
['Q1 (25%)'] + [num(s.q25) for s in stats],
|
||||
['Q3 (75%)'] + [num(s.q75) for s in stats],
|
||||
]
|
||||
|
||||
|
||||
def parse_date_time(iso: str) -> datetime:
|
||||
"""
|
||||
Parse date faster. Running 1,000,000 trials, this parse_date function is 4.03 times faster than
|
||||
python's built-in dateutil.parser.isoparse() function.
|
||||
|
||||
Preconditions:
|
||||
- iso is the output of datetime.isoformat() (In a format like "2021-10-20T23:50:14")
|
||||
- iso is a valid date (this function does not check for the validity of the input)
|
||||
|
||||
:param iso: Input date
|
||||
:return: Datetime object
|
||||
"""
|
||||
return datetime(int(iso[:4]), int(iso[5:7]), int(iso[8:10]),
|
||||
int(iso[11:13]), int(iso[14:16]), int(iso[17:19]))
|
||||
|
||||
|
||||
def parse_date_only(iso: str) -> datetime:
|
||||
"""
|
||||
Parse date faster.
|
||||
|
||||
Preconditions:
|
||||
- iso starts with the format of "YYYY-MM-DD" (e.g. "2021-10-20" or "2021-10-20T10:04:14")
|
||||
- iso is a valid date (this function does not check for the validity of the input)
|
||||
|
||||
:param iso: Input date
|
||||
:return: Datetime object
|
||||
"""
|
||||
return datetime(int(iso[:4]), int(iso[5:7]), int(iso[8:10]))
|
||||
|
||||
|
||||
def daterange(start_date: str, end_date: str) -> Generator[tuple[str, datetime], None, None]:
|
||||
"""
|
||||
Date range for looping, excluding the end date
|
||||
|
||||
Preconditions:
|
||||
- start_date starts with the "YYYY-MM-DD" format
|
||||
- end_date starts with the "YYYY-MM-DD" format
|
||||
|
||||
:param start_date: Start date in "YYYY-MM-DD" format
|
||||
:param end_date: Ending date in "YYYY-MM-DD" format
|
||||
:return: Generator for looping through the dates one day at a time.
|
||||
"""
|
||||
start = parse_date_only(start_date)
|
||||
for n in range(int((parse_date_only(end_date) - start).days)):
|
||||
dt = start + timedelta(n)
|
||||
yield dt.strftime('%Y-%m-%d'), dt
|
||||
|
||||
|
||||
def map_to_dates(y: dict[str, Union[int, float]], dates: list[str],
|
||||
default: float = 0) -> list[float]:
|
||||
"""
|
||||
Takes y-axis data in the form of a mapping of dates to values, and returns a list of all the
|
||||
values mapped to the date in dates. If a date in dates isn't in y, then the default values is
|
||||
used instead.
|
||||
|
||||
Preconditions:
|
||||
- The date in dates must be in the same format as the dates in the keys of y
|
||||
|
||||
:param y: Y axis data (in the format y[date] = value)
|
||||
:param dates: Dates
|
||||
:param default: Default data if y doesn't exist on that date
|
||||
:return: A list of y data, one over each day in dates
|
||||
"""
|
||||
return [y[d] if d in y else default for d in dates]
|
||||
|
||||
|
||||
def filter_days_avg(y: list[float], n: int) -> list[float]:
|
||||
"""
|
||||
Filter y by taking an average over an n-days window. If n = 0, then return y without processing.
|
||||
|
||||
Preconditions:
|
||||
- n % 2 == 1
|
||||
- len(y) > 0
|
||||
|
||||
>>> actual = filter_days_avg(list(range(10)), 3)
|
||||
>>> expected = [1/3, 1, 2, 3, 4, 5, 6, 7, 8, 26/3]
|
||||
>>> all(math.isclose(actual[i], expected[i]) for i in range(10))
|
||||
True
|
||||
|
||||
>>> actual = filter_days_avg(list(range(10)), 5)
|
||||
>>> expected = [0.6, 1.2, 2, 3, 4, 5, 6, 7, 7.8, 8.4]
|
||||
>>> all(math.isclose(actual[i], expected[i]) for i in range(10))
|
||||
True
|
||||
|
||||
:param y: Values
|
||||
:param n: Number of days, must be odd
|
||||
:return: Averaged data
|
||||
"""
|
||||
if n <= 1:
|
||||
return y
|
||||
if n % 2 != 1:
|
||||
ValueError(f'n must be odd (you entered {n})')
|
||||
|
||||
# Sliding window; maintain a sum of an interval centered around i
|
||||
# if the interval exceeds the beginning/end, pretend that the first/last elements are "extended"
|
||||
|
||||
radius = n // 2
|
||||
current_sum = (radius + 1) * y[0] # current sum is sum(y[-r:0] + y[0:1] + y[1:r + 1])
|
||||
for i in range(radius):
|
||||
i = min(i, len(y) - 1)
|
||||
current_sum += y[i] # adding the values in y[1:r + 1]
|
||||
|
||||
ret = []
|
||||
# python_ta says "unnecessary indexing" because the index only accesses items from 1 list.
|
||||
# But a look at the code tells you that iterating is not sufficient for the sliding window,
|
||||
# random access is actually better, because prior elements need to be accessed as well, and
|
||||
# using a queue seems too silly. The other option is to literally extend the array but that
|
||||
# takes extra space instead of O(1) space, so why do that?
|
||||
for i in range(len(y)):
|
||||
left, right = i - radius, i + radius
|
||||
left = max(0, left) # avoid index out of bounds by "extending" first/last element
|
||||
right = min(right, len(y) - 1)
|
||||
current_sum += y[right] # extend sliding window
|
||||
ret.append(current_sum / n)
|
||||
current_sum -= y[left] # remove old values
|
||||
return ret
|
||||
|
||||
|
||||
def divide_zeros(numerator: list[float], denominator: list[float]) -> list[float]:
|
||||
"""
|
||||
Divide two lists of floats, ignoring zeros (anything dividing by zero will produce zero)
|
||||
|
||||
Preconditions:
|
||||
- len(numerator) == len(denominator)
|
||||
|
||||
:param numerator: Numerator
|
||||
:param denominator: Denominator
|
||||
:return: A list where list[i] = numerator[i] / denominator[i]
|
||||
"""
|
||||
output = np.zeros(len(numerator), float)
|
||||
for i in range(len(numerator)):
|
||||
if denominator[i] == 0:
|
||||
output[i] = 0
|
||||
else:
|
||||
output[i] = numerator[i] / denominator[i]
|
||||
# This marks it as incorrect type, but it's actually not incorrect type, just because numpy
|
||||
# doesn't specify its return types
|
||||
# noinspection PyTypeChecker
|
||||
return output.tolist()
|
||||
|
||||
|
||||
class EnhancedJSONEncoder(json.JSONEncoder):
|
||||
"""
|
||||
An improvement to the json.JSONEncoder class, which supports:
|
||||
encoding for dataclasses, encoding for datetime, and sets
|
||||
"""
|
||||
|
||||
def default(self, o: object) -> object:
|
||||
|
||||
# Support encoding dataclasses
|
||||
# https://stackoverflow.com/a/51286749/7346633
|
||||
if dataclasses.is_dataclass(o):
|
||||
return dataclasses.asdict(o)
|
||||
|
||||
# Support encoding datetime
|
||||
if isinstance(o, (datetime, date)):
|
||||
return o.isoformat()
|
||||
|
||||
# Support for sets
|
||||
# https://stackoverflow.com/a/8230505/7346633
|
||||
if isinstance(o, set):
|
||||
return list(o)
|
||||
|
||||
return super().default(o)
|
||||
|
||||
|
||||
def json_stringify(obj: object, indent: Union[int, None] = None) -> str:
|
||||
"""
|
||||
Serialize json string with support for dataclasses and datetime and sets and with custom
|
||||
configuration.
|
||||
|
||||
Preconditions:
|
||||
- obj != None
|
||||
|
||||
:param obj: Objects
|
||||
:param indent: Indent size or none
|
||||
:return: Json strings
|
||||
"""
|
||||
return json.dumps(obj, indent=indent, cls=EnhancedJSONEncoder, ensure_ascii=False)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
doctest.testmod()
|
||||
# python_ta.contracts.check_all_contracts()
|
||||
python_ta.check_all(config={
|
||||
'extra-imports': ['dataclasses', 'doctest', 'inspect', 'json', 'os', 'statistics', 'math',
|
||||
'datetime', 'pathlib', 'typing', 'json5', 'numpy', 'tabulate',
|
||||
'constants'], # the names (strs) of imported modules
|
||||
'allowed-io': ['load_config', 'write', 'debug', 'read'],
|
||||
'max-line-length': 100,
|
||||
'disable': ['R1705', 'C0200', 'E9994', 'W0611']
|
||||
}, output='pyta_report.html')
|
||||
@@ -1,586 +0,0 @@
|
||||
"""CSC110 Fall 2021 Project
|
||||
This module uses matplotlib to visualize processed data as graphs. The results are stored in
|
||||
report directory.
|
||||
The graphs are created after processing the data, for example with filtering and removing outliers.
|
||||
"""
|
||||
|
||||
import os.path
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Optional, Union
|
||||
|
||||
import matplotlib.dates as mdates
|
||||
import matplotlib.ticker
|
||||
import scipy.signal
|
||||
from matplotlib import pyplot as plt, font_manager
|
||||
|
||||
import python_ta
|
||||
import python_ta.contracts
|
||||
|
||||
from collect_others import get_covid_cases_us
|
||||
from constants import RES_DIR, REPORT_DIR
|
||||
from processing import load_tweets, load_user_sample
|
||||
from utils import debug, daterange, map_to_dates, filter_days_avg, Reporter, remove_outliers, \
|
||||
tabulate_stats, get_statistics
|
||||
|
||||
|
||||
@dataclass()
|
||||
class UserFloat:
|
||||
"""
|
||||
Model for which a floating point data is assigned to each user
|
||||
|
||||
This is used for both COVID tweet frequency and popularity ratio data, because both of these
|
||||
are floating point data.
|
||||
|
||||
Attributes:
|
||||
- name: Twitter user's screen name
|
||||
- data: The float data that's associated with this user
|
||||
|
||||
Representation Invariants:
|
||||
- self.name != ''
|
||||
"""
|
||||
name: str
|
||||
data: float
|
||||
|
||||
|
||||
class Sample:
|
||||
"""
|
||||
A sample of many users, containing statistical data that will be used in graphs.
|
||||
|
||||
Attributes:
|
||||
- name: Sample name
|
||||
- users: List of user screen names in this sample
|
||||
- user_freqs: Total frequencies of all posts for each user across all dates (sorted)
|
||||
- user_pops: Total popularity ratios of all posts for each user across all dates (sorted)
|
||||
- user_all_pop_avg: Average popularity of all u's posts
|
||||
- user_date_covid_pop_avg: Average popularity of COVID tweets by a specific user on a date
|
||||
(user_covid_tweets_pop[user][date] = Average popularity of COVID-posts by {user} on {date})
|
||||
- date_covid_freq: Total COVID-tweets frequency on a specific date for all users.
|
||||
- dates: dates[i] = The i-th day since the first tweet
|
||||
- date_freqs: date_freqs[i] = COVID frequency of all posts from all sampled users on date[i]
|
||||
- date_pops: date_pops[i] = Average pop-ratio of all posts from all sampled users on date[i]
|
||||
|
||||
Representation Invariants:
|
||||
- self.name != ''
|
||||
- all(name != '' for name in self.users)
|
||||
"""
|
||||
name: str
|
||||
users: list[str]
|
||||
|
||||
user_freqs: list[UserFloat]
|
||||
user_pops: list[UserFloat]
|
||||
user_all_pop_avg: dict[str, float]
|
||||
|
||||
# user_covid_tweets_pop[user][date] = Average popularity of COVID-posts by {user} on {date}
|
||||
user_date_covid_pop_avg: dict[str, dict[str, float]]
|
||||
|
||||
date_covid_freq: dict[str, float]
|
||||
# dates[i] = The i-th day since the first tweet
|
||||
dates: list[datetime]
|
||||
# date_freqs[i] = COVID frequency of all posts from all users in this sample on date[i]
|
||||
date_freqs: list[float]
|
||||
# date_pops[i] = Average popularity ratio of all posts from all users in this sample on date[i]
|
||||
date_pops: list[float]
|
||||
|
||||
def __init__(self, name: str, users: list[str]) -> None:
|
||||
self.name = name
|
||||
self.users = users
|
||||
self.calculate_sample_data()
|
||||
self.calculate_change_data()
|
||||
|
||||
def calculate_sample_data(self) -> None:
|
||||
"""
|
||||
This function loads and calculates the frequency that a list of user posts about COVID, and
|
||||
also calculates their relative popularity of COVID posts.
|
||||
|
||||
This function also creates a combined list of all users in a sample.
|
||||
|
||||
Frequency: the frequency that the sampled users post about COVID. For example, someone who
|
||||
posted every single tweet about COVID will have a frequency of 1, and someone who doesn't
|
||||
post about COVID will have a frequency of 0.
|
||||
|
||||
Popularity ratio: the relative popularity of the sampled users' posts about COVID. If one
|
||||
person posted a COVID post and got 1000 likes, while their other posts (including this
|
||||
one) got an average of 1 like, they will have a relative popularity of 1000. If,
|
||||
on the other hand, one person posted a COVID post and got 1 like, while their other posts
|
||||
(including this one) got an average of 1000 likes, they will have a relative popularity
|
||||
of 1/1000.
|
||||
|
||||
To prevent divide-by-zero, we ignored everyone who didn't post about covid and who didn't
|
||||
post at all.
|
||||
|
||||
Preconditions:
|
||||
- Downloaded tweets data are sorted by date
|
||||
"""
|
||||
debug(f'Calculating sample tweets data for {self.name}...')
|
||||
popularity = []
|
||||
frequency = []
|
||||
date_covid_count = {}
|
||||
date_all_count = {}
|
||||
self.user_all_pop_avg = {}
|
||||
self.user_date_covid_pop_avg = {}
|
||||
for i in range(len(self.users)):
|
||||
u = self.users[i]
|
||||
|
||||
# Show progress
|
||||
if i != 0 and i % 100 == 0:
|
||||
debug(f'- Calculated {i} users.')
|
||||
|
||||
# Load processed tweet
|
||||
tweets = load_tweets(u)
|
||||
# Ignore retweets, and ignore tweets that are earlier than the start of COVID
|
||||
tweets = [t for t in tweets if not t.repost and t.date > '2020-01-01T01:01:01']
|
||||
# Filter covid tweets
|
||||
covid = [t for t in tweets if t.covid_related]
|
||||
|
||||
# To prevent divide by zero, ignore people who didn't post at all
|
||||
if len(tweets) == 0:
|
||||
frequency.append(UserFloat(u, 0))
|
||||
continue
|
||||
# Calculate the frequency of COVID-related tweets
|
||||
frequency.append(UserFloat(u, len(covid) / len(tweets)))
|
||||
|
||||
# Calculate date fields
|
||||
# Assume tweets are sorted
|
||||
# tweets.sort(key=lambda x: x.date)
|
||||
# Calculate popularity by date
|
||||
date_cp_sum = {}
|
||||
date_cp_count = {}
|
||||
for t in tweets:
|
||||
d = t.date[:10]
|
||||
|
||||
# For covid popularity on date
|
||||
if t.covid_related:
|
||||
if d not in date_cp_sum:
|
||||
date_cp_sum[d] = 0
|
||||
date_cp_count[d] = 0
|
||||
date_cp_sum[d] += t.popularity
|
||||
date_cp_count[d] += 1
|
||||
|
||||
# For frequency on date
|
||||
if d not in date_covid_count:
|
||||
date_covid_count[d] = 0
|
||||
date_all_count[d] = 0
|
||||
if t.covid_related:
|
||||
date_covid_count[d] += 1
|
||||
date_all_count[d] += 1
|
||||
|
||||
self.user_date_covid_pop_avg[u] = \
|
||||
{date: date_cp_sum[date] / date_cp_count[date] for date in date_cp_sum}
|
||||
|
||||
# Calculate total popularity ratio for a user
|
||||
# To prevent divide by zero, ignore everyone who didn't post about covid
|
||||
if len(covid) == 0:
|
||||
continue
|
||||
# Get the average popularity for COVID-related tweets
|
||||
covid_pop_avg = sum(tweet.popularity for tweet in covid) / len(covid)
|
||||
all_pop_avg = sum(tweet.popularity for tweet in tweets) / len(tweets)
|
||||
# Save global_avg
|
||||
self.user_all_pop_avg[u] = all_pop_avg
|
||||
# To prevent divide by zero, ignore everyone who literally have no likes on any post
|
||||
if all_pop_avg == 0:
|
||||
continue
|
||||
# Get the relative popularity
|
||||
popularity.append(UserFloat(u, covid_pop_avg / all_pop_avg))
|
||||
|
||||
# Calculate frequency on date
|
||||
self.date_covid_freq = {date: date_covid_count[date] / date_all_count[date] for date in
|
||||
date_covid_count}
|
||||
|
||||
# Sort by relative popularity or frequency
|
||||
popularity.sort(key=lambda x: x.data, reverse=True)
|
||||
frequency.sort(key=lambda x: x.data, reverse=True)
|
||||
|
||||
# Assign to sample
|
||||
self.user_freqs = frequency
|
||||
self.user_pops = popularity
|
||||
debug('- Done.')
|
||||
|
||||
def calculate_change_data(self) -> None:
|
||||
"""
|
||||
This function calculates self.date_freqs and self.date_pops, which are lists that stores the
|
||||
frequencies and popularity ratios on each date since the first tweet. This calculation
|
||||
ignores users, but instead combines the tweets of the entire sample in the calculation.
|
||||
|
||||
More details about the calculations can be found in the report, or report_document.md
|
||||
|
||||
Preconditions:
|
||||
- len(self.tweets) > 0
|
||||
- self.tweets != None
|
||||
|
||||
:return: None
|
||||
"""
|
||||
self.dates = []
|
||||
self.date_pops = []
|
||||
|
||||
# Average popularity ratio results over 7 days
|
||||
seven_days_user_prs = []
|
||||
|
||||
# Loop through all dates from the start of COVID to when the data is obtained
|
||||
for (ds, dt) in daterange('2020-01-01', '2021-11-25'):
|
||||
self.dates.append(dt)
|
||||
|
||||
# Calculate date covid popularity ratio
|
||||
users_posted_today = [u for u in self.users if u in self.user_date_covid_pop_avg
|
||||
and ds in self.user_date_covid_pop_avg[u]]
|
||||
if len(users_posted_today) == 0:
|
||||
seven_days_user_prs.append([])
|
||||
else:
|
||||
user_prs = [self.user_date_covid_pop_avg[u][ds] / self.user_all_pop_avg[u]
|
||||
for u in users_posted_today if self.user_all_pop_avg[u] != 0]
|
||||
seven_days_user_prs.append(user_prs)
|
||||
|
||||
# Average over seven days
|
||||
# python_ta thinks user_prs is being shadowed here but it's not because the other
|
||||
# instance is stuck in the else statement above
|
||||
# python_ta also thinks that user_prs is possibly not defined here
|
||||
# but it's in a comprehension so it is
|
||||
seven_days_count = sum(len(user_prs) for user_prs in seven_days_user_prs)
|
||||
if seven_days_count == 0:
|
||||
pops_i = 1
|
||||
else:
|
||||
user_pop_ratio_sum = sum(sum(user_prs) for user_prs in seven_days_user_prs)
|
||||
pops_i = user_pop_ratio_sum / seven_days_count
|
||||
|
||||
# More than seven days, remove one
|
||||
if len(seven_days_user_prs) > 7:
|
||||
seven_days_user_prs.pop(0)
|
||||
|
||||
self.date_pops.append(pops_i)
|
||||
|
||||
# Date frequencies
|
||||
self.date_freqs = map_to_dates(self.date_covid_freq,
|
||||
[x.isoformat()[:10] for x in self.dates])
|
||||
self.date_freqs = filter_days_avg(self.date_freqs, 3)
|
||||
|
||||
|
||||
def load_samples() -> list[Sample]:
|
||||
"""
|
||||
Load samples, and report demographics
|
||||
|
||||
:return: Samples
|
||||
"""
|
||||
# Load sample, convert format
|
||||
users = load_user_sample()
|
||||
samples = [Sample('500-pop', [u.username for u in users.most_popular]),
|
||||
Sample('500-rand', [u.username for u in users.random]),
|
||||
Sample('eng-news', list(users.english_news))]
|
||||
|
||||
# Report demographics
|
||||
keys = ['en', 'zh', 'ja']
|
||||
pop_lang = [u.lang for u in users.most_popular]
|
||||
rand_lang = [u.lang for u in users.random]
|
||||
Reporter('sample-demographics.md') \
|
||||
.table([['`500-pop`'] + [str(len(pop_lang))] + [str(pop_lang.count(k)) for k in keys],
|
||||
['`500-rand`'] + [str(len(rand_lang))] + [str(rand_lang.count(k)) for k in keys]],
|
||||
['Total', 'English', 'Chinese', 'Japanese'], False)
|
||||
|
||||
return samples
|
||||
|
||||
|
||||
def report_top_20_tables(sample: Sample) -> None:
|
||||
"""
|
||||
Get top-20 most frequent or most relatively popular users and store them in a table.
|
||||
|
||||
:param sample: Sample
|
||||
:return: None
|
||||
"""
|
||||
Reporter(f'freq/{sample.name}-top-20.md').table(
|
||||
[[u.name, f'{u.data * 100:.1f}%'] for u in sample.user_freqs[:20]],
|
||||
['Username', 'Frequency'])
|
||||
|
||||
Reporter(f'pop/{sample.name}-top-20.md').table(
|
||||
[[u.name, f'{u.data * 100:.1f}%'] for u in sample.user_pops[:20]],
|
||||
['Username', 'Popularity Ratio'])
|
||||
|
||||
|
||||
def report_ignored(samples: list[Sample]) -> None:
|
||||
"""
|
||||
Report how many people didn't post about covid or posted less than 1% about COVID across
|
||||
different samples.
|
||||
|
||||
And for popularity ratios, report how many people are ignored because they didn't post.
|
||||
|
||||
:param samples: Samples
|
||||
:return: None
|
||||
"""
|
||||
# For frequencies, report who didn't post
|
||||
table = [["Total users"] + [str(len(s.users)) for s in samples],
|
||||
["Users who didn't post at all"]
|
||||
+ [str(len([1 for a in s.user_freqs if a.data == 0])) for s in samples],
|
||||
["Users who posted less than 1%"]
|
||||
+ [str(len([1 for a in s.user_freqs if a.data < 0.01])) for s in samples]]
|
||||
|
||||
Reporter('freq/didnt-post.md').table(table, [s.name for s in samples], True)
|
||||
|
||||
# For popularity ratio, report ignored
|
||||
table = [["Ignored"] + [str(len(s.users) - len(s.user_pops)) for s in samples]]
|
||||
Reporter('pop/ignored.md').table(table, [s.name for s in samples], True)
|
||||
|
||||
|
||||
def graph_load_font() -> None:
|
||||
"""
|
||||
Load iosevka font for matplotlib
|
||||
"""
|
||||
font = os.path.join(RES_DIR, 'iosevka-ss04-regular.ttf')
|
||||
fe = font_manager.FontEntry(font, 'iosevka')
|
||||
font_manager.fontManager.ttflist.insert(0, fe)
|
||||
plt.rcParams["font.family"] = "iosevka"
|
||||
|
||||
|
||||
def graph_histogram(x: list[float], path: str, title: str, freq: bool, clear_outliers: bool = False,
|
||||
bins: int = 20) -> None:
|
||||
"""
|
||||
Plot a histogram
|
||||
|
||||
:param x: X axis data
|
||||
:param path: Output image path (should end in .png)
|
||||
:param title: Title
|
||||
:param freq: Whether we are graphing frequencies data instead of popularity ratios
|
||||
:param clear_outliers: Remove outliers or not
|
||||
:param bins: Number of bins
|
||||
:return: None
|
||||
"""
|
||||
if clear_outliers:
|
||||
title = title + ' - No Outliers'
|
||||
x = remove_outliers(x)
|
||||
|
||||
border_color = '#5b3300'
|
||||
|
||||
# Create fig ax
|
||||
fig: plt.Figure
|
||||
ax: plt.Axes
|
||||
fig, ax = plt.subplots()
|
||||
ax.margins(x=0, y=0)
|
||||
|
||||
# Plot
|
||||
ax.set_title(title, color=border_color)
|
||||
ax.hist(x, bins=bins, color='#ffcccc')
|
||||
|
||||
if freq:
|
||||
ax.xaxis.set_major_formatter(matplotlib.ticker.PercentFormatter(1))
|
||||
else:
|
||||
ax.axvline(1, color='#DACAA9')
|
||||
|
||||
# Colors
|
||||
ax.tick_params(color=border_color, labelcolor=border_color)
|
||||
for spine in ax.spines.values():
|
||||
spine.set_edgecolor(border_color)
|
||||
|
||||
# Grid
|
||||
ax.grid(visible=True, axis='both')
|
||||
|
||||
# Save
|
||||
fig.savefig(os.path.join(REPORT_DIR, path))
|
||||
fig.clf()
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
def graph_line_plot(x: list[datetime], y: Union[list[float], list[list[float]]], path: str,
|
||||
title: str, freq: bool, n: int = 0, labels: Optional[list[str]] = None) -> None:
|
||||
"""
|
||||
Plot a line plot, and reduce noise using an IIR filter
|
||||
|
||||
:param x: X axis data
|
||||
:param y: Y axis data (or Y axis data lines)
|
||||
:param n: IIR filter parameter (Ignored if n <= 0)
|
||||
:param path: Output image path (should end in .png)
|
||||
:param freq: Whether you are graphing frequencies data instead of popularity ratios
|
||||
:param title: Title
|
||||
:param labels: Labels or none
|
||||
:return: None
|
||||
"""
|
||||
# Filter
|
||||
if n > 0:
|
||||
y = scipy.signal.lfilter([1.0 / n] * n, 1, y)
|
||||
|
||||
border_color = '#5b3300'
|
||||
|
||||
# Create fig ax
|
||||
fig: plt.Figure
|
||||
ax: plt.Axes
|
||||
fig, ax = plt.subplots()
|
||||
ax.margins(x=0, y=0)
|
||||
|
||||
# Date format
|
||||
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))
|
||||
ax.xaxis.set_major_formatter(mdates.DateFormatter('%m\n%Y'))
|
||||
ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=1))
|
||||
ax.xaxis.set_minor_formatter(mdates.DateFormatter('%m'))
|
||||
|
||||
if freq:
|
||||
# Y axis percent format
|
||||
ax.yaxis.set_major_formatter(matplotlib.ticker.PercentFormatter(1))
|
||||
|
||||
# Plot
|
||||
ax.set_title(title, color=border_color)
|
||||
|
||||
# Plotting single data line
|
||||
if isinstance(y[0], float):
|
||||
ax.plot(x, y, color='#d4b595')
|
||||
|
||||
if freq:
|
||||
# Color below curve
|
||||
ax.fill_between(x, y, color='#d4b595')
|
||||
|
||||
else:
|
||||
ax.axhline(1, color=border_color)
|
||||
ax.set_ylim(0, 2)
|
||||
|
||||
# Plotting multiple data lines
|
||||
else:
|
||||
fig.set_size_inches(16, 9)
|
||||
plt.tight_layout()
|
||||
for i in range(len(y)):
|
||||
line, = ax.plot(x, y[i])
|
||||
if len(labels) > i:
|
||||
line.set_label(labels[i])
|
||||
ax.legend()
|
||||
|
||||
# Plotting frequency, add in the COVID cases data
|
||||
if freq:
|
||||
c = map_to_dates(get_covid_cases_us(), [d.isoformat()[:10] for d in x])
|
||||
c = filter_days_avg(c, 7)
|
||||
c = scipy.signal.lfilter([1.0 / n] * n, 1, c)
|
||||
|
||||
twin: plt.Axes = ax.twinx()
|
||||
twin.plot(x, c, color='#d4b595', label='US COVID-19 Cases')
|
||||
twin.set_ylim(bottom=0)
|
||||
|
||||
# Plotting popularity
|
||||
else:
|
||||
ax.axhline(1, color=border_color)
|
||||
ax.set_ylim(0, 2)
|
||||
|
||||
# Colors
|
||||
ax.tick_params(color=border_color, labelcolor=border_color)
|
||||
ax.tick_params(which='minor', colors='#e1ad6b', labelcolor='#e1ad6b')
|
||||
for spine in ax.spines.values():
|
||||
spine.set_edgecolor(border_color)
|
||||
|
||||
# Grid
|
||||
ax.grid(visible=True, axis='both')
|
||||
|
||||
# Save
|
||||
path = Path(os.path.join(REPORT_DIR, path))
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
fig.savefig(str(path))
|
||||
fig.clf()
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
def report_histograms(sample: Sample) -> None:
|
||||
"""
|
||||
Report histograms of COVID posting frequencies and popularity ratios
|
||||
|
||||
:param sample: Sample
|
||||
:return: None
|
||||
"""
|
||||
x = [f.data for f in sample.user_freqs]
|
||||
title = f'COVID-related posting frequency for {sample.name}'
|
||||
graph_histogram(x, f'freq/{sample.name}-hist-outliers.png', title, True, False, 100)
|
||||
x = [p for p in x if p > 0.001]
|
||||
graph_histogram(x, f'freq/{sample.name}-hist.png', title, True, True)
|
||||
|
||||
x = [f.data for f in sample.user_pops]
|
||||
title = f'Popularity ratio of COVID posts for {sample.name}'
|
||||
graph_histogram(x, f'pop/{sample.name}-hist.png', title, False, True)
|
||||
|
||||
|
||||
def report_stats(samples: list[Sample]) -> None:
|
||||
"""
|
||||
Report frequencies and popularity ratios' statistics
|
||||
|
||||
:param samples: Samples
|
||||
:return: None
|
||||
"""
|
||||
xs = [[d.data for d in s.user_pops] for s in samples]
|
||||
|
||||
table = tabulate_stats([get_statistics(x) for x in xs])
|
||||
Reporter('pop/stats-with-outliers.md').table(table, [s.name for s in samples], True)
|
||||
|
||||
table = tabulate_stats([get_statistics(remove_outliers(x)) for x in xs])
|
||||
Reporter('pop/stats.md').table(table, [s.name for s in samples], True)
|
||||
|
||||
xs = [[d.data for d in s.user_freqs if d.data > 0.0005] for s in samples]
|
||||
table = tabulate_stats([get_statistics(x) for x in xs], percent=True)
|
||||
Reporter('freq/stats.md').table(table, [s.name for s in samples], True)
|
||||
|
||||
|
||||
def report_change_different_n(sample: Sample) -> None:
|
||||
"""
|
||||
Experiment wth different n values for IIR filter
|
||||
|
||||
:param sample: Sample
|
||||
:return: None
|
||||
"""
|
||||
for n in [5, 10, 15]:
|
||||
graph_line_plot(sample.dates, sample.date_pops, f'change/n/{n}.png',
|
||||
f'COVID-posting popularity ratio over time for {sample.name} IIR(n={n})',
|
||||
False, n)
|
||||
|
||||
|
||||
def report_change_graphs(sample: Sample) -> None:
|
||||
"""
|
||||
Report COVID-posting popularity ratio vs. time and COVID-posting frequency vs time,
|
||||
both with IIR(10) filter
|
||||
|
||||
:param sample: Sample
|
||||
:return: None
|
||||
"""
|
||||
graph_line_plot(sample.dates, sample.date_pops, f'change/pop/{sample.name}.png',
|
||||
f'COVID-posting popularity ratio over time for {sample.name} IIR(10)',
|
||||
False, 10)
|
||||
graph_line_plot(sample.dates, sample.date_freqs, f'change/freq/{sample.name}.png',
|
||||
f'COVID-posting frequency over time for {sample.name} IIR(10)',
|
||||
True, 10)
|
||||
|
||||
|
||||
def report_all() -> None:
|
||||
"""
|
||||
Generate all reports
|
||||
|
||||
Preconditions:
|
||||
- Twitter data have been downloaded and processed.
|
||||
"""
|
||||
graph_load_font()
|
||||
|
||||
Path(f'{REPORT_DIR}/freq').mkdir(parents=True, exist_ok=True)
|
||||
Path(f'{REPORT_DIR}/pop').mkdir(parents=True, exist_ok=True)
|
||||
|
||||
debug('Loading samples...')
|
||||
samples = load_samples()
|
||||
|
||||
print()
|
||||
debug('Creating reports...')
|
||||
|
||||
report_ignored(samples)
|
||||
report_stats(samples)
|
||||
for s in samples:
|
||||
report_top_20_tables(s)
|
||||
report_histograms(s)
|
||||
report_change_graphs(s)
|
||||
report_change_different_n(samples[0])
|
||||
|
||||
# python_ta thinks that s is shadowing again but the other instance is in the for loop above
|
||||
# or in another comprehension so clearly there is no shadowing
|
||||
graph_line_plot(samples[0].dates, [s.date_pops for s in samples], 'change/comb/pop.png',
|
||||
'COVID-posting popularity ratio over time for all samples - IIR(10)', False, 10,
|
||||
labels=[s.name for s in samples])
|
||||
graph_line_plot(samples[0].dates, [s.date_freqs for s in samples], 'change/comb/freq.png',
|
||||
'COVID-posting frequency over time for all samples - IIR(10)', True, 10,
|
||||
labels=[s.name for s in samples])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# python_ta.contracts.check_all_contracts()
|
||||
python_ta.check_all(config={
|
||||
'extra-imports': ['os.path', 'dataclasses', 'datetime', 'pathlib', 'typing', 'matplotlib',
|
||||
'matplotlib.dates', 'matplotlib.ticker', 'scipy.signal', 'collect_others',
|
||||
'processing', 'constants', 'utils'
|
||||
], # the names (strs) of imported modules
|
||||
'allowed-io': ['report_all'], # the names (strs) of functions that call print/open/input
|
||||
'max-line-length': 100,
|
||||
'disable': ['R1705', 'C0200', 'E9988', 'E9969', 'R0902', 'R1702', 'R0913']
|
||||
}, output='pyta_report.html')
|
||||
@@ -1,69 +0,0 @@
|
||||
\documentclass[fontsize=11pt]{article}
|
||||
\usepackage{amsmath}
|
||||
\usepackage[utf8]{inputenc}
|
||||
\usepackage[margin=0.75in]{geometry}
|
||||
|
||||
\title{CSC110 Project Proposal: COVID-19 Discussion Trend Analysis}
|
||||
\author{Azalea Gui \& Peter Lin}
|
||||
\date{Friday, November 5, 2021}
|
||||
\usepackage[
|
||||
backend=biber,
|
||||
style=numeric,
|
||||
citestyle=apa,
|
||||
sorting=nyt
|
||||
]{biblatex}
|
||||
\addbibresource{references.bib}
|
||||
\DeclareNameAlias{author}{last-first}
|
||||
|
||||
\begin{document}
|
||||
\maketitle
|
||||
|
||||
\section*{Problem Description and Research Question}
|
||||
|
||||
\indent
|
||||
|
||||
We have observed that there have been increasingly more voices talking about COVID-19 since the start of the pandemic. However, with recent policy changes in many countries aiming to limit the effect of COVID-19, it is unclear how people’s discussions would react. Some people might be inclined to believe that the pandemic is starting to end so that discussing it would seem increasingly like an unnecessary effort. In contrast, others might find these policy changes controversial and want to voice their opinions on them even more. Also, even though COVID-related topics are almost always on the news, some news outlets might intentionally cover them more frequently than others. For the people watching the news, some people might find these news reports interesting, while others can’t help but switch channels. So, how people’s interest in listening about or discussing COVID-related topics changes over time is not very clear. \textbf{Our goal is to analyze how people’s interest in COVID-related topics changes and how frequently people have discussed COVID-related issues in the two years since the pandemic started.} Also, different social media platforms might induce people to view the pandemic differently. For example, we don’t know whether people on open social media platforms such as Twitter, where everyone can view your posts, might be more or less inclined to post or COVID-related content than people on closed social media platforms such as Instagram, Wechat, or Telegram. Also, people or news outlets with different numbers of followers or viewers might have different inclinations too. \textbf{So, we also aim to compare people’s interests in posting about COVID-related topics between platforms and popularity.}
|
||||
|
||||
\section*{Dataset Description}
|
||||
|
||||
\indent
|
||||
|
||||
Our data will come from individuals’ discussions on many social media or chatting platforms. Some social media platforms provide complete APIs, such as Twitter, which is why we plan to use Twitter as our primary data source. We will gather and analyze textual data from the tweets of famous or important individuals and compare data between individuals. We will also combine these data into meaningful groups to find if any grouping traits will produce meaningful differences. We will also analyze tweets from random individuals to create a broader image that can be generalized to the entire platform’s social environment. We have attached the processed data from twitter user \texttt{voxdotcom} as an example. All data containing tweet contents will not be included in our final report. Our final report will only be based on whether or not the sample is COVID-related.
|
||||
|
||||
Another group of data will come from significant news publishers such as New York Times or Guardian News. We plan only to use the title, publishing date, and publisher of the news reports, and we will use the title only to determine whether or not the news report is COVID-related.
|
||||
|
||||
We are also curious about the frequency of discussion in other countries not relying on Twitter, such as in China, where the government blocks Twitter from the internet. So, we will also analyze news and media from China. We will gather data from popular Chinese telegram channels as well.
|
||||
|
||||
We also plan to gather countries’ confirmed case data from Johns Hopkins CSSE because plotting discussion frequency against confirmed cases might be more meaningful than plotting it against date.
|
||||
|
||||
\section*{Computational Plan}
|
||||
|
||||
\subsection*{Data Gathering}
|
||||
|
||||
\indent
|
||||
|
||||
We plan to transform different platforms’ user posting data, all with unique formats, into data in a platform-independent data model to store and compare. When processing social media data, we will convert platform-dependent keywords such as \texttt{favorites}, \texttt{retweets}, or \texttt{full\_text} on Twitter and \texttt{content}, \texttt{views}, or \texttt{comments} on Telegram into our unique platform-independent model with keywords such as \texttt{popularity} and \texttt{text}. And we will store all processed data in \textbf{JSON} before analysis. As for the raw data from different social media platforms, we plan to gather Twitter data using the \textbf{Tweepy} library and Telegram channels data using \textbf{python-telegram-bot}. However, unfortunately, there are no known libraries for Wechat Moments. We will try to obtain Wechat data through package capture using pyshark, but that might not be successful.
|
||||
|
||||
For news outlet data, we plan to use \textbf{requests} to obtain raw HTML from different listing sites, extract news articles’ titles, publishers, and publishing dates with \textbf{regex}, and store them using JSON. We will convert different HTML formats from different news publishers’ sites into our platform-independent news model.
|
||||
|
||||
We also use the \textbf{Json5} library to parse configurations and API keys of our data gathering and analysis programs.
|
||||
|
||||
\subsection*{Data Analysis/Visualization}
|
||||
|
||||
\indent
|
||||
|
||||
We plan to use \textbf{matplotlib} to create data images or \textbf{plotly} to create websites for data visualization. We plan to use \textbf{NumPy} for statistical calculations.
|
||||
|
||||
To identify whether or not some article is about COVID, we currently use a keyword search. However, a keyword search might not be accurate when COVID has became such an essential background to our society (i.e. many articles with the word COVID in them are about something else). We might experiment with training a binary classification model with \textbf{Keras} and \textbf{scikit-learn} to better classify COVID articles. We might also experiment with training autoencoders with vectorized word occurence data in an COVID-related article to find if there are significant categories within COVID articles (i.e. some COVID articles might be about new COVID policies, and others might just be general updates relating to COVID, and this might be an important insight because people's interests in these different types of COVID articles might differ).
|
||||
|
||||
The primary type of graph we will use will be a frequency histogram——an individual or a group of data’s frequency of mentioning COVID-related topics will be graphed against the date from January 1, 2020, to Nov 1, 2021. We will experiment with group sizes and classification methods to find which variables influence the frequency and which don’t. (For example, we will group individuals by popularity and compare between groups to find if popularity impacts the frequency they mention COVID-related topics). We also plan to overlay these charts in comparison to visualize the statistical differences better.
|
||||
|
||||
Another variant of the frequency histogram will be plotted not against the date but against the country’s confirmed cases since people’s emotions of anxiety might be influenced by the growing or decreasing of confirmed cases. We will also graph some data using this variant to find more insights.
|
||||
|
||||
|
||||
% \section*{References}
|
||||
% Generate references automatically from references.bib.
|
||||
\nocite{*}
|
||||
\printbibliography
|
||||
|
||||
\end{document}
|
||||
@@ -1,14 +0,0 @@
|
||||
@misc{matplotlib, title={Overview}, url={https://matplotlib.org/stable/contents.html}, journal={Overview - Matplotlib 3.4.3 documentation}, author={Hunter, John and Droettboom , Michael and Firing, Eric and Dale, Darren}, year={2021}, month={Aug}}
|
||||
@misc{plotly, title={Plotly python graphing library}, url={https://plotly.com/python/}, journal={Plotly}, publisher={Plotly Technologies Inc.}, author={Plotly}, year={2015}}
|
||||
@misc{json5, title={JSON5}, url={https://pypi.org/project/json5/}, journal={PyPI}, author={Pranke, Dirke}, year={2021}}
|
||||
@misc{tweepy, title={Tweepy documentation}, url={https://docs.tweepy.org/en/stable/}, journal={Tweepy Documentation - tweepy 4.3.0 documentation}, author={Roesslein, Joshua}, year={2021}}
|
||||
@misc{numpy, title={NumPy v1.21 manual}, url={https://numpy.org/doc/stable/}, journal={Overview - NumPy v1.21 Manual}, author={Numpy}, year={2021}}
|
||||
@misc{telegram, title={Welcome to python telegram bot's documentation!}, url={https://python-telegram-bot.readthedocs.io/en/stable/}, journal={python}, author={Toledo, Leandro}, year={2021}}
|
||||
@misc{keras,
|
||||
title = {Keras API Reference},
|
||||
journal = {Keras API Reference},
|
||||
url = {https://keras.io/api/},
|
||||
author = {Keras},
|
||||
year = {2015}
|
||||
}
|
||||
@misc{sklearn, title={scikit-learn 1.0.1 documentation}, url={https://scikit-learn.org/stable/modules/classes.html}, journal={API Reference - scikit-learn 1.0.1 documentation}, author={scikit-learn}, year={2010}}
|
||||
Binary file not shown.
@@ -1,132 +0,0 @@
|
||||
\documentclass{article}
|
||||
\usepackage{amsmath}
|
||||
\usepackage[utf8]{inputenc}
|
||||
\usepackage[margin=0.75in]{geometry}
|
||||
\usepackage{hyperref}
|
||||
|
||||
\title{CSC110 Project: COVID-19 Discussion Trend Analysis}
|
||||
\author{Azalea Gui \& Peter Lin}
|
||||
\date{December 5, 2021}
|
||||
\usepackage[
|
||||
backend=biber,
|
||||
style=numeric,
|
||||
citestyle=apa,
|
||||
sorting=nyt
|
||||
]{biblatex}
|
||||
\addbibresource{references.bib}
|
||||
\DeclareNameAlias{author}{last-first}
|
||||
|
||||
\newcommand{\C}{\texttt}
|
||||
|
||||
\begin{document}
|
||||
\maketitle
|
||||
|
||||
\section{Problem Description and Research Question}
|
||||
\indent
|
||||
|
||||
We have observed that there have been increasingly more voices talking about COVID-19 since the start of the pandemic. However, different groups of people might view the importance of discussing the pandemic differently. For example, we don't know whether the most popular people on Twitter will be more or less inclined to post COVID-related content than the average Twitter user. Also, while some audience finds these content interesting, others quickly scroll through them. \textbf{So, we aim to compare people's interests in posting coronavirus content and the audience's interests in viewing them between different groups.} Also, with recent developments and policy changes toward COVID-19, it is unclear how people's discussions would react. Some people might believe that the pandemic is starting to end so that discussing it would seem increasingly like an unnecessary effort, while others might find these policy changes controversial and want to voice their opinions even more. Also, even though COVID-related topics are almost always on the news, some news outlets might intentionally cover them more frequently than others. For the people watching the news, some people might find these news reports interesting, while others can't help but switch channels. So, how people's interest in listening or discussing COVID-related topics changes over time is not very clear. \textbf{Our second goal is to analyze how people's interest in COVID-related topics changes and how frequently people have discussed COVID-related issues in the two years since the pandemic started.}
|
||||
|
||||
\section{Dataset Used}
|
||||
\indent
|
||||
|
||||
\begin{itemize}
|
||||
\item[1.] A wide range of Twitter users: We used twitter's get friends list API (13) and the follows-chaining technique to obtain a wide range of twitter users. This technique is explained in the Computational Overview section. Due to rate limiting, we ran the program for one day and obtained 224,619 users (852.3 MB decompressed). However, only the username, popularity, post count, and language data are used, and the processed (filtered) user dataset \C{data/twitter/user/processed/users.json} is only 7.9 MB in total.
|
||||
|
||||
\item[2.] All tweets from sampled users: We selected two samples of 500 users each (the sampling method is explained in the Computational Overview section), and we used the user-timeline API (14) to obtain all of their tweets. Due to rate limiting, the program took around 16 hours to finish, and we obtained 6.07 GB of raw data (uncompressed). During processing, we reduced the data for each tweet to only its date, popularity (likes + retweets), whether it is a retweet, and whether it is COVID-related. The text of the tweets are not retained, and the processed data directory \C{data/twitter/user-tweets/processed} is only 107.9 MB in total.
|
||||
|
||||
\item[3.] Top 100 news twitter accounts by Bremmen (3)
|
||||
\item[4.] COVID-19 daily new cases data by New York Times (1).
|
||||
\end{itemize}
|
||||
|
||||
\section{Computational Overview}
|
||||
|
||||
\subsection*{Data Gathering \& Processing}
|
||||
\indent
|
||||
|
||||
This section explains the data gathering and processing done in \verb|collect_twitter.py|, \verb|collect_others.py|, and \verb|processing.py|. In this section, raw data will be collected and processed into the \verb|processed_data.7z| that we provided.
|
||||
|
||||
To create our samples, we collected a wide range of Twitter users using Twitter's get friends list API endpoint through \textbf{tweepy}, using the follows-chaining technique. We specified one single user as the starting point (in this case, we picked \verb|voxdotcom|). The program then obtains the user's friends list, picks 3 random users and 3 most followed users from the friend list, adds them to the queue, and starts the downloading process again from each of the six friends. Because of Twitter's rate limiting on the get friends list endpoint, we can only obtain a maximum of 200 users per minute, with many of them being duplicates. We ran the program continuously for one day and obtained 224,619 users (852.3 MB decompressed). However, only the username, popularity, post count, and language data are kept after processing (filtering). The processed user dataset \verb|data/twitter/user/processed/users.json| is 7.9 MB in total. We selected our samples by filtering the results first based on language, selected the top 500 most followed users as 500-pop, filtered the list again based on post count (>1000) and followers (>150), then selected a random sample of 500 users as 500-rand.
|
||||
|
||||
We also downloaded all tweets from our sampled users through the user-timeline API also with \textbf{tweepy}. Due to rate limiting, the program took around 16 hours to finish, and we obtained 7.7 GB of raw data (uncompressed). During processing, for each tweet, we extracted only its date, popularity (likes + retweets), whether it is a retweet, and whether it is related to the COVID-19 pandemic. The text of the tweets are not retained, and the processed data directory \verb|data/twitter/user-tweets/processed/| is 141.6 MB in total.
|
||||
|
||||
To determine whether a post is COVID-related we used keyword matching with three lists of COVID-related keywords for English, Chinese, and Japanese. Tweets with content containing these keywords are marked as COVID-related.
|
||||
|
||||
We also used the COVID-19 daily cases data published by New York Times to compare with peaks and through in our frequency over date graph, and the program gathered this data by sending an HTTP requst to New York Times' public github repository using \textbf{requests} and then parsing the CSV.
|
||||
|
||||
For submission, we packed the processed data into a 7zip archive using \textbf{py7zr}. This is necessary because our processed data are placed very close to the raw data in the folder structure, and creating the archive manually requires separating the processed data from the raw data into two separate folders first. We also used py7zr to pack our HTML resources.
|
||||
|
||||
We also used \textbf{json5} to store the configuration of this program, which contains Twitter API keys.
|
||||
|
||||
\subsection*{Statistical Visualization Generation}
|
||||
\indent
|
||||
|
||||
This section explains the statistical report generation done in \verb|visualization.py|. In this section, specific "elements" used in our report are generated. For example, an element might be an image of the user frequency graph in one of our samples, and another element might be a markdown table showing the amount of users who posted less than 1\% or didn't post in our samples. Each element is stored in a separate file, which will be included in the visualized report explained in the next section.
|
||||
|
||||
Since the statistical computations of report generation is explained in the interactive report, this section will only focus on the technical aspect of which libraries we used to complete these computations and generate the statistical elements of the report.
|
||||
|
||||
We used \textbf{matplotlib} to generate images that will be displayed in our report, including histograms and line graphs. We used \textbf{scipy} for signal filtering and smoothening the curves so that they are readable (specifically, \verb|scipy.signal.lfilter|). We used \textbf{numpy} in our statistical calculations to calculate percentile points and remove outliers. We then used \textbf{tabulate} to generate Markdown format tables for report elements.
|
||||
|
||||
\subsection*{Interactive Report Generation}
|
||||
\indent
|
||||
|
||||
This section explains the interactive report creation in \verb|report.py|.
|
||||
|
||||
We wrote our report in Markdown format, located in \verb|resources/report_document.md|. However, the default Markdown format doesn't support including the contents of other markdown files generated in the previous step, so we extended the markdown format by adding \verb|@include|, \verb|@include-lines|, and \verb|@include-cut| functionality.
|
||||
|
||||
Then, to display the markdown in a webpage, we created a template HTML (\verb|resources/report_page.html|) and used python to inject the markdown content into the HTML template. Then, we used \textbf{Marked} (a JS library) to render the Markdown to the webpage. We did not use the python Markdown library because it did not support the Github Markdown table format generated with \textbf{tabulate} in the previous step. Then, we used the \textbf{Flask} framework to serve the webpage along with the referenced assets like images, js, and fonts on an HTTP server.
|
||||
|
||||
On the webpage, we used \textbf{jQuery} (a JS library) to make the images enlargeable. We also imported \textbf{MathJax} (a JS library) to automatically render LaTeX on the webpage (no code needed to reference this library).
|
||||
|
||||
Even though the handout required the project to be purely written in Python, instructors in Piazza allowed us to use web languages as long as all data gathering, processing, computation, visualization, and image rendering are done in pure Python (@1704).
|
||||
|
||||
\section{Running Instructions}
|
||||
\indent
|
||||
|
||||
\begin{itemize}
|
||||
\item [1. ] Download the submitted files into a new folder called \verb|src|.
|
||||
|
||||
\item [2. ] Extract the archive \verb|src/resources.7z| into the folder \verb|src/resources|.
|
||||
|
||||
\item [3. ] Install \verb|src/requirements.txt|, either with PyCharm or with \texttt{pip install -r src/requirements.txt}.
|
||||
|
||||
\item [4. ] If you would like to test out our data collection code or collect data manually, do the following: (We do not recommend collecting all data manually because it took the program two days to gather our data due to rate limiting)
|
||||
|
||||
\begin{itemize}
|
||||
\item [a. ] Register for Twitter API keys on their website. For more information, look at the following link: \href{https://developer.twitter.com/en/docs/twitter-api/getting-started/getting-access-to-the-twitter-api}{(Getting access to the Twitter API)}.
|
||||
\item [b. ] Copy the Twitter API keys into the corresponding fields in \verb|src/config.json5|
|
||||
\item [c. ] In \verb|src/main.py|, uncomment all the lines of code for data collection and processing: that is, the steps C1.0-C1.2, P1-P2, C2.1-C2.3, P3.
|
||||
\end{itemize}
|
||||
|
||||
\item [5. ] If you would like to use our processed data, you can download the archive from \url{https://send.utoronto.ca} with the following code, which will expire on December 27.\\
|
||||
Claim ID: 6PPMsHQNTV7TJRmu\\
|
||||
Claim Passcode: 9VPba4YiYx2cetbU\\
|
||||
Alternatively, you can download it from a permanent link: \url{https://csc110.hydev.org/processed-data.7z}\\
|
||||
Extract the archive into a directory called \verb|data| at the same level as \verb|src|, that is, \verb|data| and \verb|src| should be in the same folder.\\
|
||||
The file \verb|src/constants.py| contains a more detailed directory tree.
|
||||
|
||||
\item [6. ] Run \verb|src/main.py|, either in PyCharm or with \texttt{python3 main.py}. Note that the execution directory should be in \verb|src| and not the root directory. If you use PyCharm, you should open \verb|src| in PyCharm instead of the root directory. This file structure is intentionally designed to prevent PyCharm from indexing \verb|data|, which takes an extremely long time.
|
||||
|
||||
\end{itemize}
|
||||
|
||||
\section{Changes to Proposal}
|
||||
\indent
|
||||
|
||||
First, we originally planned to include news reports from separate journal websites in our analysis as well. However, when we gather the data, we found that there is no way to identify the popularity of a news report published on a journal website. So, we decided to gather the tweets of news accounts on Twitter instead, which will also have the benefit of having the same data gathering and analysis process for each news channel.
|
||||
|
||||
Second, we originally planned to compare people's interests in posting COVID-related topics between different platforms because we thought Chinese people don't rely on Twitter as much since Twitter is blocked in China. However, there isn't any publically available WeChat API that we can use for analysis, and WeChat is also more private, with access to someone's postings limited to only their friends. (It is as if everyone on Twitter has a locked account). Therefore, it is impractical to gather data from WeChat. And, for Telegram channels, the postings does not have a like feature, and might not have commenting feature unless the channel host specifically set up for it using a third-party bot. So there isn't a reliable way to obtain popularity data on Telegram as well. So, instead of comparing between platforms, we compared different groups of people on the Twitter platform.
|
||||
|
||||
\section{Discussion}
|
||||
\indent
|
||||
|
||||
To try to answer our research questions, we made 3 user samples: \texttt{500-pop}, composed of the 500 most popular users on Twitter, \texttt{500-rand}, 500 random users on Twitter, and \texttt{eng-news}, the top 100 English news channels on Twitter (3).
|
||||
|
||||
Our first research question asks: \textbf{how frequently do people post about COVID-related issues, and how interested are people to see COVID-related posts?}
|
||||
After making graphs showing the frequency of COVID-related posts, the histograms showing frequency all tend to be skewed right, or decreasing. In all 3 samples of \texttt{500-pop}, \texttt{500-rand}, and \texttt{eng-news}, for every user, the majority of their posts were not COVID-related. However, news channels tended to post more about COVID than \texttt{500-pop}, who tended to post more about COVID than \texttt{500-rand}. This suggests that the \texttt{500-pop} users tend to post more than the average person about COVID, possibly because they have a large influence and want to share their opinion, but they post less about COVID than news channels, who have to report the latest news about the pandemic, regulations, and slowing the spread.
|
||||
The histograms showing the popularity ratios are also skewed right, except the news, which is almost centered. This shows that for COVID-posts outside of news channels, they usually receive poor engagement. However, for news channels, they receive almost as much engagement as their other posts, suggesting that users are indifferent to COVID-news as regular news.
|
||||
|
||||
Our second research question tackled the same thing: but instead, how it changed over time. From the line graphs showing COVID-posting frequency over time, it tends to decrease for the samples of \texttt{500-rand} and \texttt{eng-news}, although \texttt{500-pop} and \texttt{eng-news} shared a peak around December 2020 and trough around June 2021, which seems to be related to the rise and fall of COVID cases in the US. A bit surprisingly, the news has stopped posting as many COVID-related posts, maybe because they are starting to incorporate other keywords such as "Delta variant". In addition, there is a large spike in May 2021 for the sample \texttt{500-pop}, which is also around when the Delta variant started appearing. This could mean that the news started talking about that more, while the popular twitter users talked about its consequences. However, the \texttt{500-rand} sample has seen a decrease in frequency, suggesting that they post less about it, possibly because their COVID posts get less engagement, or just a general decrease in the interest in COVID as a topic. We speculate it can possibly be people getting used to seeing COVID-related news all the time, and just like background noise, are starting to ignore it (a process called habituation).
|
||||
The line graphs showing popularity ratio over time, however, were a lot messier. Despite attempts to filter out the noise, there were constants spikes in the graph throughout the entire graph, making it difficult to draw any conclusions. This could possibly be because the sample size is too small, or the method of determining popularity isn't sophisticated enough and prone to random noise. Unfortunately, this means that further research is required to answer our question.
|
||||
|
||||
\nocite{*}
|
||||
\printbibliography
|
||||
\end{document}
|
||||
@@ -1,16 +0,0 @@
|
||||
@misc{matplotlib, title={Overview}, url={https://matplotlib.org/stable/index.html}, journal={Overview - Matplotlib 3.5.0 documentation}, author={Hunter, John and Droettboom , Michael and Firing, Eric and Dale, Darren}, year={2021}, month={Aug}}
|
||||
@misc{json5, title={JSON5}, url={https://pypi.org/project/json5/}, journal={PyPI}, author={Pranke, Dirke}, year={2021}}
|
||||
@misc{tweepy, title={Tweepy documentation}, url={https://docs.tweepy.org/en/stable/}, journal={Tweepy Documentation - tweepy 4.3.0 documentation}, author={Roesslein, Joshua}, year={2021}}
|
||||
@misc{numpy, title={NumPy v1.21 manual}, url={https://numpy.org/doc/stable/}, journal={Overview - NumPy v1.21 Manual}, author={Numpy}, year={2021}}
|
||||
|
||||
@misc{tabulate, title={Tabulate}, url={https://pypi.org/project/tabulate/}, journal={PyPI}, author={Astanin, Sergey and Crespí, Pau Tallada and Marsi, Erwin and Kocikowski, Mik and Ryder, Bill and Dwiel, Zach}, year={0AD}}
|
||||
@misc{py7zr, title={Py7zr}, url={https://pypi.org/project/py7zr/}, journal={PyPI}, author={Miura, Hiroshi}, year={0AD}}
|
||||
@misc{requests, title={HTTP for Humans™}, url={https://docs.python-requests.org/en/master/index.html}, journal={Requests}, author={Reitz, Kenneth}, year={0AD}}
|
||||
@misc{beautifulsoup, title={Beautiful Soup documentation}, url={https://beautiful-soup-4.readthedocs.io/en/latest/}, journal={Beautiful Soup Documentation - Beautiful Soup 4.4.0 documentation}, author={Richardson, Leonard}, year={0AD}}
|
||||
@misc{flask, title={Flask}, url={https://pypi.org/project/Flask/}, journal={PyPI}, author={Ronacher, Armin}, year={0AD}}
|
||||
@misc{scipy, title={SciPy documentation}, url={https://scipy.github.io/devdocs/index.html}, journal={SciPy documentation - SciPy v1.9.0.dev0+1070.09b8d94 Manual}, author={The SciPy Community}, year={0AD}}
|
||||
@misc{twitter_friends, title={Get friends/list | docs | twitter developer platform}, url={https://developer.twitter.com/en/docs/twitter-api/v1/accounts-and-users/follow-search-get-users/api-reference/get-friends-list}, journal={Twitter}, publisher={Twitter}, author={Twitter}, year={0AD}}
|
||||
@misc{twitter_timeline, title={Get statuses/user\_timeline | docs | twitter developer platform}, url={https://developer.twitter.com/en/docs/twitter-api/v1/tweets/timelines/api-reference/get-statuses-user_timeline}, journal={Twitter}, publisher={Twitter}, author={Twitter}, year={0AD}}
|
||||
@misc{twitter_news, title={100 most Influential News Media Twitter Account Archives}, url={https://memeburn.com/motorburn/tag/100-most-influential-news-media-twitter-account/}, journal={Motorburn}, author={Bremmen, Nur}, year={2010}, month={Sep}}
|
||||
@misc{covid_cases, title={Nytimes/COVID-19-data: An ongoing repository of data on coronavirus cases and deaths in the U.S.}, url={https://github.com/nytimes/covid-19-data}, journal={GitHub}, author={Almukhtar, Sarah and Aufrichtig, Aliza and Barnard, Anne and Bloch, Matthew}, year={2021}}
|
||||
|
||||
Reference in New Issue
Block a user