We describe a novel observational study of the frequency and significance of social media users' profile changes. Drawing upon literature from impression management, specifically two formative theories: self-construal and signaling theory, our research examines the likelihood that users will change their profiles, what constitutes a significant profile change, and how profile changes could correspond to crisis events. Using Twitter's public API, we created a longitudinal dataset of profile snapshots for 2.3MM active Twitter profiles across a two-week period. Our findings indicate users are more likely to change their Profile Summaries and Display Names than their Locations and Screen Names (handles). Further, we used topic modeling to partition users based on their profiles to identify themes and explored how profile changes differ among these thematic groups (e.g., Trump supporters). Last, we identified the most significant word changes by users in their profiles and explored the possible effects of crisis events, in this case the Las Vegas shooting of October 2017. Using a representative sample of Twitter users, our study is the first to longitudinally examine Twitter profile changes. Our findings provide valuable baseline data for further study of Twitter profiles, including social contagion spread through these profiles and the impact of external events on users' identities.

We describe a novel longitudinal study of the frequency and significance of social media users' profile changes. Drawing upon two formative theories from communication and psychology: self-construal and signaling theory, we examine the likelihood that users will change their profiles and what constitutes a significant profile change. Our findings indicate that users are more likely to change their Profile Summaries and Display Names than their Locations and Screen Names (i.e. handles). Further, we used topic modeling to partition users based on their profiles to identify themes and explored how profile changes differ among these thematic groups (e.g., Trump supporters). Last, we identified the most significant word changes by users in their profiles. Our findings provide valuable baseline data for further study of Twitter profiles, including the spread of social contagion through these profiles.

No abstract