Social Platform Trust & Safety
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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
A Russian Bot Farm Used AI to Lie to Americans. What Now?
csis.org
need better ‘trust’ systems to prevent things like this
APA PsycNet
psycnet.apa.orgTruth Queries as misinformation intervention
Erin Kissane • Against the Dark Forest
Ari Schulman • Why Speech Platforms Can Never Escape Politics | National Affairs
A scholarly culture that reads and discusses enduringly great works incentivizes the production of such... See more
Ari Schulman • Why Speech Platforms Can Never Escape Politics | National Affairs
reputation / karma systems