Social Influence in the Academic Twitter Migration to Mastodon: A Computational Psychology Approach
This article is a preprint and has not been certified by peer review [What does this mean?].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 in late 2022, or more recently from X to Bluesky. Migrating researchers may exert influence on their colleagues to join them, but despite the growing importance of these migrations, the underlying social dynamics within academia remain poorly understood. Analyzing data from over 250,000 researchers, we used a machine learning model informed by social impact theory to identify key drivers 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, rather than individual influential strength. Additionally, researchers endorsing open science principles were approximately eleven times more likely to migrate. These findings suggest that collective efforts, tailored to group characteristics, may foster behavior change interventions in science.
Keyword(s)
academic social networks researcher behavior group dynamics computational psychology of science metasciencePersistent Identifier
Date of first publication
2025-03-05
Publisher
PsychArchives
Citation
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Bittermann et al._Academic Twitter Migration_Preprint.pdfAdobe PDF - 915.59KBMD5: b9aca43446d62b289dd977caafcf5880
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There are no other versions of this object.
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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 timestamp2025-03-05T07:25:03Z
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Made available on2025-03-05T07:25:03Z
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Date of first publication2025-03-05
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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 in late 2022, or more recently from X to Bluesky. Migrating researchers may exert influence on their colleagues to join them, but despite the growing importance of these migrations, the underlying social dynamics within academia remain poorly understood. Analyzing data from over 250,000 researchers, we used a machine learning model informed by social impact theory to identify key drivers 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, rather than individual influential strength. Additionally, researchers endorsing open science principles were approximately eleven times more likely to migrate. These findings suggest that collective efforts, tailored to group characteristics, may foster behavior change interventions in science.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
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.16155
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Language of contenteng
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PublisherPsychArchives
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Is related tohttps://hdl.handle.net/20.500.12034/8557
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Is related tohttps://hdl.handle.net/20.500.12034/9043
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Keyword(s)academic social networks
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Keyword(s)researcher behavior
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Keyword(s)group dynamics
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Keyword(s)computational psychology of science
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Keyword(s)metascience
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Dewey Decimal Classification number(s)150
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TitleSocial Influence in the Academic Twitter Migration to Mastodon: A Computational Psychology Approachen
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DRO typepreprint
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Leibniz institute name(s) / abbreviation(s)ZPID