Preprint

Academic Social Media Migration Through a Psychology of Science Lens

This article is a preprint and has not been certified by peer review [What does this mean?].

Author(s) / Creator(s)

Bittermann, André
Lauer, Tim
Peters, Fritz

Abstract / Description

Social media is an essential tool for scholarly communication, yet dissatisfaction with platforms can drive researchers to migrate elsewhere, as seen in the shift from Twitter to Mastodon or from X to Bluesky. Despite the growing importance of these migrations, the underlying social dynamics within academia remain poorly understood. Analyzing data from over 250,000 researchers with a computational psychology of science approach, we used a machine learning model informed by social impact theory and the concept of self-consistency to identify predictors of the 2022 academic Mastodon migration. Our findings reveal that the strongest predictor of platform migration was the number of followed peers discussing the move, whereas individual influential strength contributed relatively little to model predictions. Open-science endorsement was also strongly associated with migration (odds ratio ≈ 11). These findings suggest that behavior change interventions in science may be fostered by collective efforts tailored to group characteristics.

Keyword(s)

academic social networks researcher behavior group dynamics computational psychology of science metascience

Persistent Identifier

Date of first publication

2026-10-19

Publisher

PsychArchives

Citation

  • 2
    2026-10-19
    We address homophily and contagion in observational social media studies, clarify data and design limitations, emphasize our psychology of science perspective on academic social media migration and further specify our measures and operationalizations
  • 1
    2025-03-05
  • Author(s) / Creator(s)
    Bittermann, André
  • Author(s) / Creator(s)
    Lauer, Tim
  • Author(s) / Creator(s)
    Peters, Fritz
  • PsychArchives acquisition timestamp
    2026-10-19T10:04:04Z
  • Made available on
    2025-03-05T07:25:03Z
  • Made available on
    2026-10-19T10:04:04Z
  • Date of first publication
    2026-10-19
  • Abstract / Description
    Social media is an essential tool for scholarly communication, yet dissatisfaction with platforms can drive researchers to migrate elsewhere, as seen in the shift from Twitter to Mastodon or from X to Bluesky. Despite the growing importance of these migrations, the underlying social dynamics within academia remain poorly understood. Analyzing data from over 250,000 researchers with a computational psychology of science approach, we used a machine learning model informed by social impact theory and the concept of self-consistency to identify predictors of the 2022 academic Mastodon migration. Our findings reveal that the strongest predictor of platform migration was the number of followed peers discussing the move, whereas individual influential strength contributed relatively little to model predictions. Open-science endorsement was also strongly associated with migration (odds ratio ≈ 11). These findings suggest that behavior change interventions in science may be fostered by collective efforts tailored to group characteristics.
    en
  • Publication status
    other
  • Review status
    notReviewed
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/11569.2
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.22624
  • Language of content
    eng
  • Publisher
    PsychArchives
    en
  • Is related to
    https://hdl.handle.net/20.500.12034/8557
  • Is related to
    https://www.psycharchives.org/handle/20.500.12034/17960
  • Is related to
    https://hdl.handle.net/20.500.12034/9043
  • Keyword(s)
    academic social networks
    en_US
  • Keyword(s)
    researcher behavior
    en_US
  • Keyword(s)
    group dynamics
    en_US
  • Keyword(s)
    computational psychology of science
    en_US
  • Keyword(s)
    metascience
    en_US
  • Dewey Decimal Classification number(s)
    150
  • Title
    Academic Social Media Migration Through a Psychology of Science Lens
    en
  • DRO type
    preprint
    en_US
  • Leibniz institute name(s) / abbreviation(s)
    ZPID