Understanding the Diverging User Trajectories in Highly-related Online Communities during the COVID-19 Pandemic
Jason Shuo Zhang, Brian C. Keegan, Qin Lv, Chenhao Tan

TL;DR
This study analyzes user trajectories in two related COVID-19 online communities on Reddit, revealing how user engagement and community identity diverged over time during the pandemic.
Contribution
It introduces a movement analysis framework to understand membership changes and predicts user migration between highly-related online communities during a crisis.
Findings
Users' language and membership patterns diverged after March 2020.
Approximately 50% of /r/China flu members moved to /r/Coronavirus in February.
User migration can be predicted based on prior activity in other subreddits.
Abstract
As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two "new" communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the very beginning of this pandemic. With /r/Coronavirus promoted as the official community on Reddit, it remains an open question how users choose between these two highly-related communities. In this paper, we characterize user trajectories in these two communities from the beginning of COVID-19 to the end of September 2020. We show that new users of /r/China flu and /r/Coronavirus were similar from January to March. After that, their differences steadily increase, evidenced by both language distance and membership prediction, as the pandemic continues to unfold. Furthermore, users who started at /r/China flu from January to March were more…
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Taxonomy
TopicsComplex Network Analysis Techniques · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining
