Algorithm-mediated social learning in online social networks

William J. Brady*, Joshua Conrad Jackson, Björn Lindström, M. J. Crockett

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations


Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or ‘PRIME’ information) to sustain users’ attention and maximize engagement. Here, we synthesize emerging insights into ‘algorithm-mediated social learning’ and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human–algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.

Original languageEnglish (US)
Pages (from-to)947-960
Number of pages14
JournalTrends in Cognitive Sciences
Issue number10
StatePublished - Oct 2023


  • algorithms
  • norms
  • social learning
  • social media
  • social networks

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Neuropsychology and Physiological Psychology
  • Cognitive Neuroscience


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