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Cited by 132Misinformation and Its ImpactsDigital Media and PhilosophyEthics and Social Impacts of AI

Algorithm-mediated social learning in online social networks

Brady, William J., Jackson, Joshua Conrad, Lindström, Björn, Crockett, M.J.·2023·Trends in Cognitive Sciences
DOI 10.1016/j.tics.2023.06.008
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Abstract

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.

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130 of 130 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The study provides evidence for the theory of Brady et al. [13] that ranking algorithms exploit humans' natural social biases towards moralized, emotional content.

    “It also provides evidence for the theory of Brady et al. [13] who suggest that ranking algorithms exploit humans' natural social biases towards moralized, emotional content.”

    From Twitter's Algorithm: Amplifying Anger, Animosity, and Affective Polarization (2023) · cites this paper as [13]

  • The distinction between political out-groups and other societal out-groups may be an important distinction for the theory proposed by Brady et al. [13].

    “This distinction between political out-groups and other societal out-groups may also be an important distinction for the theory proposed by Brady et al. [13]”

    From Twitter's Algorithm: Amplifying Anger, Animosity, and Affective Polarization (2023) · cites this paper as [13]

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Authors
Brady, W. J., Jackson, J. C., Lindström, B., & Crockett, M. J.
Affiliations
  • Northwestern University (US)
  • Karolinska Institutet (SE)
  • Princeton University (US)
Corresponding author
Brady, William J.
ORCID
Venue
Trends in Cognitive Sciences
Publisher
Elsevier BV
Published
2023-10
Volume
27
Issue
10
Pages
947-960
ISSN
1364-6613 (ISSN-L)
DOI
10.1016/j.tics.2023.06.008
OpenAlex
W4385546082
Cited by
132
References
130
Citations by year
2026: 58 · 2025: 41 · 2024: 26 · 2023: 6 · 2021: 1
FWCI
6.66
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Trends in Cognitive Sciences (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Computer Science Applications › Digital Media and Philosophy
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
social networking sites, algorithmic amplification, misinformation spread, social conflict, human-algorithm interaction, algorithmic transparency, engagement optimization
MeSH terms
Social Learning, Algorithms, Communication, Humans, Social Networking, Social Media

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