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This article is a preprint and has not been certified by peer review [What does this mean?].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 metasciencePersistent Identifier
Date of first publication
2026-10-19
Publisher
PsychArchives
Citation
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Bittermann et al._Academic Twitter Migration_Preprint_v2.pdfAdobe PDF - 1.67MBMD5 : 0f18e5a0fa65e7b0b3595dd7e6a2d2d1
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22026-10-19We 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
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Author(s) / Creator(s)Bittermann, André
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Author(s) / Creator(s)Lauer, Tim
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Author(s) / Creator(s)Peters, Fritz
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PsychArchives acquisition timestamp2026-10-19T10:04:04Z
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Made available on2025-03-05T07:25:03Z
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Made available on2026-10-19T10:04:04Z
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Date of first publication2026-10-19
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Abstract / DescriptionSocial 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
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Publication statusother
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Review statusnotReviewed
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/11569.2
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.22624
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Language of contenteng
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PublisherPsychArchivesen
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Is related tohttps://hdl.handle.net/20.500.12034/8557
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Is related tohttps://www.psycharchives.org/handle/20.500.12034/17960
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Is related tohttps://hdl.handle.net/20.500.12034/9043
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Keyword(s)academic social networksen_US
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Keyword(s)researcher behavioren_US
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Keyword(s)group dynamicsen_US
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Keyword(s)computational psychology of scienceen_US
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Keyword(s)metascienceen_US
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Dewey Decimal Classification number(s)150
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TitleAcademic Social Media Migration Through a Psychology of Science Lensen
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DRO typepreprinten_US
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Leibniz institute name(s) / abbreviation(s)ZPID