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Hongfan Lu

Publications and source records attributed to Hongfan Lu.

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The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement

We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify which posts were shown to desktop social media users, randomly assigning 9,386 users to a control group or one of five alternative ranking algorithms which simultaneously altered content across three platforms for six months during the US 2024 presidential election. This reduced our preregistered index of affective polarization by an average of 0.03 standard deviations (p < 0.05), including a 1.5 degree decrease in differences between the 100 point inparty and outparty feeling thermometers. We saw reductions in active use time for Facebook (-0.37 min/day) and Reddit (-0.2 min/day), but an increase of 0.32 min/day (p < 0.01) for X/Twitter. We saw an increase in reports of negative social media experiences but found no effects on well-being, news knowledge, outgroup empathy, perceptions of and support for partisan violence. This implies that bridging content can improve some societal outcomes without necessarily conflicting with the engagement-driven business model of social media.

cs.SI

Understanding Privacy Norms Around LLM-Based Chatbots: A Contextual Integrity Perspective

LLM-driven chatbots like ChatGPT have created large volumes of conversational data, but little is known about how user privacy expectations are evolving with this technology. We conduct a survey experiment with 300 US ChatGPT users to understand emerging privacy norms for sharing chatbot data. Our findings reveal a stark disconnect between user concerns and behavior: 82% of respondents rated chatbot conversations as sensitive or highly sensitive - more than email or social media posts - but nearly half reported discussing health topics and over one-third discussed personal finances with ChatGPT. Participants expressed strong privacy concerns (t(299) = 8.5, p < .01) and doubted their conversations would remain private (t(299) = -6.9, p < .01). Despite this, respondents uniformly rejected sharing personal data (search history, emails, device access) for improved services, even in exchange for premium features worth $200. To identify which factors influence appropriate chatbot data sharing, we presented participants with factorial vignettes manipulating seven contextual factors. Linear mixed models revealed that only the transmission factors such as informed consent, data anonymization, or the removal of personally identifiable information, significantly affected perceptions of appropriateness and concern for data access. Surprisingly, contextual factors including the recipient of the data (hospital vs. tech company), purpose (research vs. advertising), type of content, and geographic location did not show significant effects. Our results suggest that users apply consistent baseline privacy expectations to chatbot data, prioritizing procedural safeguards over recipient trustworthiness. This has important implications for emerging agentic AI systems that assume user willingness to integrate personal data across platforms.

cs.CY