Analyzing the Impact of User Behaviors on the Popularity of Tweets: A Use Case from Masking Conversations During the Covid-19 Pandemic

Abstract

The Covid-19 pandemic has unleashed an infodemic of misinformation especially about important health measures such as vaccines and masks. Social media companies have struggled to keep up with identifying content that separates these falsehoods from the volumes of information that is shared over their platforms. Because automated detection approaches can only reach moderate accuracy (∼80%), some manual examination of the content to separate misinformation becomes necessary. This manual assessment can be efficient if it is limited to only those posts that are likely to be successful in gaining popularity. Predicting the future popularity of posts is certainly a function of their content, but it also depends on the actions of the users. In this paper, we analyze which users’ actions are significantly correlated with the popularity of their tweets, where the popularity is assessed using the numbers of likes and retweets. The investigation is conducted on a year-long data gathered by sampling Twitter conversations on the controversial issue of face masks during the acute, first year of the pandemic. User parameters are grouped into two – those that involve including various artifacts in the tweets to boost their popularity, and those that represent how users interact with other users and their content. After providing the context by which these short- and long-term actions build social relationships which help drive popularity, Pearson's correlation coefficients between these parameters and the numbers of likes and retweets are computed, along with their statistical significance. Our results indicate that the artifacts that users incorporate into their tweets including hashtags, mentions, URLs, and media have no significant influence on their popularity compared to how they interact with other users. Moreover, users may like other users’ tweets when they share follower-followee (impersonal) relationships, but they look for stronger, trusted friendships to actively retweet other users’ content. Thus, “liking” a tweet may be considered a much more casual endorsement compared to “retweeting”. These findings contradict observations from the pre-Covid era, perhaps suggesting that online behaviors during the pandemic may have altered fundamentally, underscoring the need for further research.