Please note: This master’s thesis presentation will take place online.
Wei Hu, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Kate Larson
Retweeting posts is Twitter’s most important feature, playing a vital role in enabling the platform to be a virtual town hall that fosters timely discussions. This attribute has been instrumental in drawing a younger, wealthier, and more educated user-base, distinguishing Twitter from its competitors. We were motivated by the observation that the retweet count on popular tweets diminishes over time. In particular, this reduction is greater for contentious tweets. Since, retweets represent endorsements, it is pertinent to understand how self-moderation and platform moderation play a role in their retractions.
We collected our own datasets and tracked various reasons for retweet loss over time. Leveraging Kaggle datasets, we trained models to predict which tweets would see a significant decrease in retweets; the model’s performance extended to previously unseen datasets. Additionally, we proposed an algorithm to estimate the timeline of retweet loss and explored factors that contribute to individual unretweeting behaviour. Finally, our data collection period coincided with the volatile phase on Twitter following Elon Musk’s acquisition. As a result, we were able to observe the impact of various changes in platform moderation through our analysis.