Social Platform Trust & Safety
Hate Thy Neighbor: Online Hate in Local Communities
Henry Farrell • We're Getting the Social Media Crisis Wrong
There 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
“The boundary between public and private is part of the conflict between public spheres. For example, until recently, only feminists though domestic violence was a public concern, rather than a private matter. Democratic publicity therefore requires positive guarantees of opportunities for minorities to convince others of what qualifies as common
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