From 07f479e0d4b99afb9db284f21c338764b00ece5b Mon Sep 17 00:00:00 2001 From: Hykilpikonna Date: Sat, 27 Nov 2021 17:26:15 -0500 Subject: [PATCH] [U] Update report --- src/report/report_document.md | 22 ++++++---------------- src/utils.py | 2 +- 2 files changed, 7 insertions(+), 17 deletions(-) diff --git a/src/report/report_document.md b/src/report/report_document.md index 387bfc2..05ea004 100644 --- a/src/report/report_document.md +++ b/src/report/report_document.md @@ -123,13 +123,13 @@ Then, the calculated frequency data for each user in a sample are graphed in his 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 +@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. ## Results - COVID-19 Popularity Ratios -Similar histograms and statistics are calculated for user's popularity ratios in their sample: +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:
hist
@@ -137,7 +137,9 @@ Similar histograms and statistics are calculated for user's popularity ratios in
hist
-@include-lines `/pop/stats.md` 0 1 2 3 4 5 +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 # Change Analysis @@ -181,19 +183,7 @@ _**TODO**_: A conclusion * Why are these findings important? What do they reveal? * Connect to larger theme? -## Scratch pad (TODO) - -For the posting frequency, it is surprising that all three of our samples have posting frequencies dropped significantly after June 2020, as if everyone silently agreed to talk less about it. - -Possible reasons: - -* There might not be as many "breaking news" or new information anymore, as three months is probably enough that everyone are aware of the virus. -* People realized that COVID-19 is not going to be a disaster that fade away quickly and got used to it, so people paid less attention because our attention is very limited. (**TODO**: Possible psychological explanation? -- Look into how long people have paid attention to an unexpected disaster on average. Compare attention with historical disasters like 911?) -* ~~Chinese people might be relieved because new daily cases are already controlled to two digits since April.~~ (Doesn't explain why `eng-news` declined) -* ~~Reopening in China around June.~~ (Doesn't explain why `eng-news` declined) -* ~~The first time cases seem to decline in U.S.~~ (Cases went back to increasing one month later but the posting frequency didn't go back up.) - -**_TODO_** +## TODO * [ ] Frequency/time: Maybe there's a reason to the May 2021 peak? * [ ] Followers (x) vs COVID-related posts (y) scatter plot, each point is a user diff --git a/src/utils.py b/src/utils.py index bdf6316..f69bff1 100644 --- a/src/utils.py +++ b/src/utils.py @@ -207,7 +207,7 @@ def get_statistics(points: list[float]) -> Stats: q75, q25 = np.percentile(points, [75, 25]) iqr = q75 - q25 return Stats(statistics.mean(points), statistics.stdev(points), statistics.median(points), - iqr, q75, q25) + iqr, q25, q75) def tabulate_stats(stats: list[Stats], percent: bool = False) -> list[list[str]]: