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Social media platforms provide users with a profile description field, commonly known as a bio, where they can present themselves to the world. A growing literature shows that text in these bios can improve our understanding of online self-presentation and behavior, but existing work relies exclusively on keyword-based approaches to do so. We here propose and evaluate a suite of simple, effective, and theoretically motivated approaches to embed bios in spaces that capture salient dimensions of social meaning, such as age and partisanship. We evaluate our methods on four tasks, showing that the strongest one out-performs several practical baselines. We then show the utility of our method in helping understand associations between self-presentation and the sharing of URLs from low-quality news sites on Twitter, with a particular focus on explore the interactions between age and partisanship, and exploring the effects of self-presentations of religiosity. Our work provides new tools to help computational social scientists make use of information in bios, and provides new insights into how misinformation sharing may be perceived on Twitter. 

Using a novel dataset of 590M messages by 21M users, we present the first large-scale examination of the behavior of likely Bernie supporters on Twitter during the 2020 U.S. Democratic primaries and presidential election. We use these data to dispel empirically the notion of a unified, stereotypical Bernie supporter (e.g., the “Bernie Bro”). Instead, our work uncovers significant variation in the identities and ideologies of Bernie supporters who were active on Twitter. Our work makes three contributions to the literature on social media and social movements. Methodologically, we present a novel mixed methods approach to surface identity and ideological variation within a movement via use of patterns in who retweets whom (i.e. who retweets which other users) and who retweets what (i.e. who retweets which specific tweets). Substantively, documentation of these variations challenges a trend in the social movement literature to assume actors within a particular movement are unified in their ideology, identity, and values. 

In the past decade, a number of sophisticated AI-powered systems and tools have been developed and released to the scientific community and the public. These technical developments have occurred against a backdrop of political and social upheaval that is both magnifying and magnified by public health and macroeconomic crises. These technical and socio-political changes offer multiple lenses to contextualize (or distort) scientific reflexivity. Further, to computational social scientists who study computer-mediated human behavior, they have implications on what we study and how we study it. How should the ICWSM community engage with this changing world? Which disruptions should we embrace, and which ones should we resist? Whom do we ally with, and for what purpose? In this workshop co-located with ICWSM, we invited experience-based perspectives on these questions with the intent of drafting a collective research agenda for the computational social science community. We did so via the facilitation of collaborative position papers and the discussion of imminent challenges we face in the context of, for example, proprietary large language models, an increasingly unwieldy peer review process, and growing issues in data collection and access. This document presents a summary of the contributions and discussions in the workshop.