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Social media has been at the center of discussions about political polarization in the United States. However, scholars are actively debating both the scale of political polarization online, and how important online polarization is to the offline world. One question at the center of this debate is what interactions across parties look like online, and in particular 1) whether increasing the number of such interactions is likely to increase or reduce polarization, and 2) what technological affordances may make it more likely that these cross-party interactions benefit, rather than detract from, existing political challenges. The present work aims to provide insights into the latter; that is, we focus on providing a better understanding of how a set of 400,000 partisan users on a particular social media platform, Twitter, used the platform's affordances to interact within and across parties in a large dataset of tweets about COVID in 2021. Our findings suggest that Republican use of cross-party interaction were both more potent and potentially more strategic during COVID, that cross-party interaction was driven heavily by a small set of users and conversations, and that there exist non-obvious indirect pathways to cross-party exposure when different modes of interaction are chained together (especially retweets of quotes). These findings have implications beyond Twitter, we believe, in understanding how affordances of platforms can help to shape partisan exposure and interaction.

 

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. 

Therapeutic foster care agencies provide temporary placements and a range of services to at-risk youth to help ensure their safety, permanency, and wellbeing. The practitioners that plan such care operate under heavy caseloads, limited resources, and high stakes. There is significant interest in supporting these practitioners with various technological interventions, but their work and the context around it is still poorly understood. This study aims to better understand the current assessment and treatment planning work in therapeutic foster care. We used the abstraction hierarchy modeling approach to outline the purposes, values, constraints, processes, and tools that define the workplace ecology encountered by care coordinators and clinicians from therapeutic foster care programs at Hillside, a collaborating human service organization. The resulting abstraction hierarchy was closely examined to identify areas for interventions and design implications.

 

Two-sided matching markets have long existed to pair agents in the absence of regulated exchanges. A common example is school choice, where a matching mechanism uses student and school preferences to assign students to schools. In such settings, forming preferences is both difficult and critical. Prior work has suggested various prediction mechanisms that help agents make decisions about their preferences. Although often deployed together, these matching and prediction mechanisms are almost always analyzed separately. The present work shows that at the intersection of the two lies a previously unexplored type of strategic behavior: agents returning to the market (e.g., schools) can attack future predictions by interacting short-term non-optimally with their matches. Here, we first introduce this type of strategic behavior, which we call an adversarial interaction attack. Next, we construct a formal economic model that captures the feedback loop between prediction mechanisms designed to assist agents and the matching mechanism used to pair them. Finally, in a simplified setting, we prove that returning agents can benefit from using adversarial interaction attacks and gain progressively more as the trust in and accuracy of predictions increases. We also show that this attack increases inequality in the student population. 

Insight into one’s own cognitive abilities is one important aspect of metacognition. Whether this insight varies between groups when discerning true and false information has yet to be examined. We investigated whether demographics like political partisanship and age were associated with discernment ability, metacognitive efficiency, and response bias for true and false news. Participants rated the veracity of true and false news headlines and provided confidence ratings for each judgment. We found that Democrats and older adults were better at discerning true and false news than Republicans and younger adults. However, all demographic groups maintained good insight into their discernment ability. Although Republicans were less accurate than Democrats, they slightly outperformed Democrats in metacognitive efficiency when a politically equated item set was used. These results suggest that even when individuals mistake misinformation to be true, they are aware that they might be wrong.

 

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.