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Reranking partisan animosity in algorithmic social media feeds alters affective polarization.

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Exposure to antidemocratic and partisan content on social media significantly increases partisan animosity. This study developed a new method to independently evaluate how feed algorithms impact political polarization.

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Area of Science:

  • Social media algorithms
  • Political science
  • Computational social science

Background:

  • Social media platforms control data on feed-ranking algorithms.
  • Independent research on algorithmic effects is limited.
  • Understanding algorithmic impact on political polarization is crucial.

Purpose of the Study:

  • To develop a platform-independent method for studying social media feed-ranking algorithms.
  • To investigate the causal effect of exposure to antidemocratic and partisan animosity (AAPA) content on affective polarization.
  • To enable real-time, naturalistic evaluation of algorithmic interventions.

Main Methods:

  • Developed a platform-independent method to rerank social media feeds in real time.
  • Conducted a 10-day field experiment with 1256 participants on X during the 2024 US presidential campaign.
  • Utilized a large language model to rerank AAPA content exposure.

Main Results:

  • Decreasing or increasing AAPA exposure shifted out-party partisan animosity by over 2 points (on a 100-point scale).
  • No detectable differences in effects were observed across political party lines.
  • Provided causal evidence that AAPA content exposure alters affective polarization.

Conclusions:

  • The developed method allows for independent study of feed algorithms without platform cooperation.
  • Algorithmic exposure to AAPA content directly influences affective polarization.
  • Enables real-world evaluation of ranking interventions in naturalistic settings.