New research from IDI’s Pranav Goel and David Lazer investigates how social media users can repurpose factually true information from reliable sources to advance misleading narratives. They demonstrate this prevalent form of misinformation by identifying articles from reliable sources that are frequently co-shared with (shared by users who also shared) ‘fake’ news on social media, and concurrently extracting narratives present in fake news content and claims fact checked as false. They find that narratives present in misinformation content are significantly more likely to occur in co-shared articles than in articles from the same reliable sources that are not co-shared, consistent with users using information from mainstream sources to enhance the credibility and reach of potentially misleading claims.

New research from Laura Edelson finds that Facebook posts removed for violating community standards or other reasons had already reached at least three-quarters of their predicted audience by the time they were taken down.

“On Facebook, content moderation doesn’t have much impact on user experience because it happens too late,” says Edelson.

A new paper from Kai-Cheng Yang aims to assess how platforms make data available under the DSA, we conducted a comprehensive survey followed by in-depth interviews with 19 researchers to understand their experiences with data access in this new era. The team’s findings reveal significant challenges in accessing social media data, with researchers facing multiple barriers including complex API application processes, difficulties obtaining credentials, and limited API usability. These challenges have exacerbated existing institutional, regional, and financial inequities in data access. Based on these insights, they provide actionable recommendations for platforms, researchers, and policymakers to foster more equitable and effective data access, while encouraging broader dialogue within the CSCW community around interdisciplinary and multi-stakeholder solutions.

In a new study form IDI’s Myojung Chung, researchers investigated students’ aversion to AI grading systems compared to human professors, focusing on how dissatisfaction with the current evaluation system and grade outcomes affect fairness perceptions. Drawing from 228 college students in South Korea, the experiment tested three hypotheses: (1) students show AI reluctance, (2) dissatisfaction with the current system mitigates the AI reluctance, and (3) this mitigation is contingent on whether students receive high or low grades. Results confirm a general aversion to AI graders. et, students who are dissatisfied with the status quo system displayed increased preference for AI graders, especially when receiving low grades. In contrast, those receiving high grades continued to prefer human professors regardless of dissatisfaction.