Social Platform Trust & Safety
exploring the bounds of social platforms’ responsibility to keep the spaces safe
Social Platform Trust & Safety
The resulting problems are not primarily problems of disinformation, though disinformation plays some role. They are the problems you get when large swathes of the public sphere are exclusively owned by wannabe God-Emperors. Elon Musk owns X/Twitter outright. Mark Zuckerberg controls Meta through a system in which he is CEO, chairman and effective... See more
Henry Farrell • We're Getting the Social Media Crisis Wrong
Probably fake - but shows how incomplete “verification” as a trust system is
Do ppl trust fact checking orgs?
It is because a universal public square cannot be a community that the parameters of the online speech debate are stuck. The conflict between the “marketplace of ideas” framework and the “communal norms” framework seems irresolvable because it is irresolvable.
Ari Schulman • Why Speech Platforms Can Never Escape Politics | National Affairs
Social Media Algorithms Can Shape Affective Polarization via ...
arxiv.orgThere is widespread concern about the negative impacts of social media feed ranking algorithms on political polarization. Leveraging advancements in large language models (LLMs), we develop an approach to re-rank feeds in real-time to test the effects of content that is likely to polarize: expressions of antidemocratic attitudes and partisan animosity (AAPA). In a preregistered 10-day field exper- iment on X/Twitter with 1,256 consented participants, we increase or decrease participants’ exposure to AAPA in their algorithmically curated feeds. We observe more positive outparty feelings when AAPA exposure is decreased and more neg- ative outparty feelings when AAPA exposure is increased. Exposure to AAPA content also results in an immediate increase in negative emotions, such as sadness and anger. The interventions do not significantly impact traditional engagement metrics such as re-post and favorite rates. These findings highlight a potential pathway for developing feed algorithms that mitigate affective polarization by addressing content that undermines the shared values required for a healthy democracy
Supporting the Digital Safety of At-Risk Users: Lessons Learned from 9+ Years of Research & Training
dl.acm.orgSome users may not want to see any potentially abusive content at all, while others may, for example, need to sift through banal personal attacks in order to track credible death threats. The point, however, is that users themselves should be able to exercise significantly more agency over their own social media experience.