[O] Restructure file
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Hykilpikonna
@@ -34,6 +34,89 @@ class Sample:
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tweets: list[Posting] = field(default_factory=list)
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def load_samples() -> list[Sample]:
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"""
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Load samples and calculate their data
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:return: Samples
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"""
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# Load sample, convert format
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samples = load_user_sample()
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samples = [Sample('500-pop', [u.username for u in samples.most_popular]),
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Sample('500-rand', [u.username for u in samples.random]),
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Sample('eng-news', list(samples.english_news))]
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# Calculate frequencies and popularity ratios
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for s in samples:
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s.frequencies, s.popularity_ratios, s.tweets = calculate_sample_data(s.users)
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return samples
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def calculate_sample_data(users: list[str]) -> tuple[list[UserFloat], list[UserFloat], list[Posting]]:
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"""
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This function loads and calculates the frequency that a list of user posts about COVID, and
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also calculates their relative popularity of COVID posts.
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This function also creates a combined list of all users in a sample.
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Frequency: the frequency that the sampled users post about COVID. For example, someone who
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posted every single tweet about COVID will have a frequency of 1, and someone who doesn't
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post about COVID will have a frequency of 0.
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Popularity ratio: the relative popularity of the sampled users' posts about COVID. If one
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person posted a COVID post and got 1000 likes, while their other posts (including this one) got
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an average of 1 like, they will have a relative popularity of 1000. If, on the other hand, one
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person posted a COVID post and got 1 like, while their other posts (including this one) got an
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average of 1000 likes, they will have a relative popularity of 1/1000.
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To prevent divide-by-zero, we ignored everyone who didn't post about covid and who didn't post
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at all.
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:param users: Users in a sample
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:return: Frequencies, Popularity ratios, Combined tweets list for the sample
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"""
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popularity = []
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frequency = []
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all_tweets: list[Posting] = []
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for u in users:
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# Load processed tweet
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tweets = load_tweets(u)
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# Ignore retweets
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tweets = [t for t in tweets if not t.repost]
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all_tweets += tweets
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# Filter covid tweets
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covid = [t for t in tweets if t.covid_related]
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# To prevent divide by zero, ignore people who didn't post at all
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if len(tweets) == 0:
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continue
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# Calculate the frequency of COVID-related tweets
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freq = len(covid) / len(tweets)
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frequency.append(UserFloat(u, freq))
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# To prevent divide by zero, ignore everyone who didn't post about covid
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if len(covid) == 0 or len(tweets) == 0:
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continue
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# Get the average popularity for COVID-related tweets
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covid_avg = statistics.mean(t.popularity for t in covid)
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global_avg = statistics.mean(t.popularity for t in tweets)
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# Get the relative popularity
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popularity.append(UserFloat(u, covid_avg / global_avg))
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# Sort by relative popularity or frequency
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popularity.sort(key=lambda x: x[1], reverse=True)
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frequency.sort(key=lambda x: x[1], reverse=True)
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# Sort by date, latest first
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all_tweets.sort(key=lambda x: x.date, reverse=True)
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# Ignore tweets that are earlier than the start of COVID
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all_tweets = [t for t in all_tweets if t.date > '2020-01-01T01:01:01']
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return frequency, popularity, all_tweets
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def view_covid_tweets_freq(sample: Sample) -> None:
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"""
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@@ -118,89 +201,6 @@ def view_covid_tweets_pop(sample: Sample) -> None:
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plt.savefig(f'{REPORT_DIR}/2-covid-tweet-popularity/{sample.name}.png')
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def load_samples() -> list[Sample]:
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"""
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Load samples and calculate their data
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:return: Samples
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"""
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# Load sample, convert format
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samples = load_user_sample()
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samples = [Sample('500-pop', [u.username for u in samples.most_popular]),
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Sample('500-rand', [u.username for u in samples.random]),
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Sample('eng-news', list(samples.english_news))]
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# Calculate frequencies and popularity ratios
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for s in samples:
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s.frequencies, s.popularity_ratios, s.tweets = calculate_sample_data(s.users)
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return samples
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def calculate_sample_data(users: list[str]) -> tuple[list[UserFloat], list[UserFloat], list[Posting]]:
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"""
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This function loads and calculates the frequency that a list of user posts about COVID, and
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also calculates their relative popularity of COVID posts.
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This function also creates a combined list of all users in a sample.
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Frequency: the frequency that the sampled users post about COVID. For example, someone who
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posted every single tweet about COVID will have a frequency of 1, and someone who doesn't
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post about COVID will have a frequency of 0.
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Popularity ratio: the relative popularity of the sampled users' posts about COVID. If one
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person posted a COVID post and got 1000 likes, while their other posts (including this one) got
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an average of 1 like, they will have a relative popularity of 1000. If, on the other hand, one
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person posted a COVID post and got 1 like, while their other posts (including this one) got an
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average of 1000 likes, they will have a relative popularity of 1/1000.
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To prevent divide-by-zero, we ignored everyone who didn't post about covid and who didn't post
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at all.
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:param users: Users in a sample
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:return: Frequencies, Popularity ratios, Combined tweets list for the sample
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"""
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popularity = []
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frequency = []
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all_tweets: list[Posting] = []
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for u in users:
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# Load processed tweet
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tweets = load_tweets(u)
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# Ignore retweets
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tweets = [t for t in tweets if not t.repost]
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all_tweets += tweets
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# Filter covid tweets
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covid = [t for t in tweets if t.covid_related]
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# To prevent divide by zero, ignore people who didn't post at all
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if len(tweets) == 0:
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continue
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# Calculate the frequency of COVID-related tweets
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freq = len(covid) / len(tweets)
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frequency.append(UserFloat(u, freq))
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# To prevent divide by zero, ignore everyone who didn't post about covid
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if len(covid) == 0 or len(tweets) == 0:
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continue
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# Get the average popularity for COVID-related tweets
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covid_avg = statistics.mean(t.popularity for t in covid)
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global_avg = statistics.mean(t.popularity for t in tweets)
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# Get the relative popularity
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popularity.append(UserFloat(u, covid_avg / global_avg))
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# Sort by relative popularity or frequency
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popularity.sort(key=lambda x: x[1], reverse=True)
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frequency.sort(key=lambda x: x[1], reverse=True)
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# Sort by date, latest first
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all_tweets.sort(key=lambda x: x.date, reverse=True)
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# Ignore tweets that are earlier than the start of COVID
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all_tweets = [t for t in all_tweets if t.date > '2020-01-01T01:01:01']
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return frequency, popularity, all_tweets
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def view_covid_tweets_date(tweets: list[Posting]):
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# Graph histogram
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plt.title(f'COVID posting dates')
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