Drivers of social influence in the Twitter migration to Mastodon
Open accessAbstract
The migration of Twitter users to Mastodon following Elon Musk’s acquisition presents a unique opportunity to study collective behavior and gain insights into the drivers of coordinated behavior in online media. We analyzed the social network and the public conversations of about 75,000 migrated users and observed that the temporal trace of their migrations is compatible with a phenomenon of social influence, as described by a compartmental epidemic model of information diffusion. Drawing from prior research on behavioral change, we delved into the factors that account for variations of the effectiveness of the influence process across different Twitter communities. Communities in which the influence process unfolded more rapidly exhibit lower density of social connections, higher levels of signaled commitment to migrating, and more emphasis on shared identity and exchange of factual knowledge in the community discussion. These factors account collectively for 57% of the variance in the observed data. Our results highlight the joint importance of network structure, commitment, and psycho-linguistic aspects of social interactions in characterizing grassroots collective action, and contribute to deepen our understanding of the mechanisms that drive processes of behavior change of online groups.
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An alternative, decentralized paradigm for social media has emerged, primarily centered around protocols that seek to reconfigure how power is distributed across a network of actors.
“In response, an alternative, decentralized paradigm for social media has emerged, primarily centered around protocols that seek to reconfigure how power is distributed across a network of actors [22,58,85].”
From Seeing the Politics of Decentralized Social Media Protocols (2026) · cites this paper as [22]
The Introduction (page unavailable) describes the growth of decentralized social networks and says Mastodon lets communities independently manage servers connected through a common protocol. This supports the emergence of a decentralized alternative, but the paper does not characterize the broader paradigm as primarily centered on protocols designed to redistribute power across actors. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPrior work on decentralized social media has often focused on user behavior within individual implementations of protocols-particularly mastodon.social-and frequently treats decentralization as a given or as a purely technical property.
“Prior work on decentralized social media has often focused on user behavior within individual implementations of protocols-particularly mastodon.social-andfrequently treats decentralization as a given [15,22], or as a purely technical property.”
From Seeing the Politics of Decentralized Social Media Protocols (2026) · cites this paper as [22]
The Introduction describes Mastodon as a decentralized alternative to Twitter, treating its decentralization as a platform characteristic. But the Introduction and Discussion describe this paper as a study of migration across Mastodon instances, not user behavior within mastodon.social in particular. They also do not establish the broader claim about what prior work has often focused on. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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Xu, W., & Sasahara, K. (2025) · Journal of Computational Social Science · doi:10.1007/s42001-025-00439-y · Cited by 1 · see citing works
Fediverse Sharing: Cross-Platform Interaction Dynamics Between Threads and Mastodon Users
Jeong, U., Beigi, A., Tahir, A., Tang, S. X., Bernard, H. R., & Liu, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-14107-1_5 · Cited by 1 · see citing works
Quian, A., García, X. L., & Pérez, X. S. (2024) · Revista Latina de Comunicación Social · doi:10.4185/rlcs-2025-2338
Gassmann, L., McConville, R., & Edwards, M. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825842
Exposure and Adoption of Social Dimensions During the Ukraine War
Sliwa, K., Klug, A.-L., Kušen, E., & Strembeck, M. (2024) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams64316.2024.10883816
Strategic leadership and radical change: from Twitter to X
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Regression-based Bibliometric Studies Based on Citespace
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Everywhere, everything: Social media and emergency medicine pharmacist education
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User Voting Behaviour in Reward-Based Social NetworksPreprint
Galdeman, A., Zignani, M., Aiello, L. M., & Gaito, S. (2025) · Research Square · doi:10.21203/rs.3.rs-8907613/v1
Self-citationUnderstanding Cross-Platform Links in User Profiles: A Data-Driven Analysis on Mastodon
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Participant-invariant, evolving patterns of influence in dynamic networks
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Exploring value concerns in social media issue reports: a human-LLM study of Bluesky and Mastodon
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Polarized Communities in Mastodon: Insights from Instance-Level Analysis (Discussion Paper)
Cava, L. L., Mandaglio, D., & Tagarelli, A. (2025) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat67162.2025.00103
Self-citationDemirel, S. (2026) · SELÇUK ÜNİVERSİTESİ İLETİŞİM FAKÜLTESİ AKADEMİK DERGİSİ · doi:10.18094/josc.1806626
- Authors
- Cava, L. L., Aiello, L. M., & Tagarelli, A.
- Affiliations
- University of Calabria (IT)
- IT University of Copenhagen (DK)
- Corresponding author
- Cava, Lucio La
- Venue
- Scientific Reports
- Published
- 2023-12-07
- Volume
- 13
- Issue
- 1
- Pages
- 21626-21626
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10703911
- OpenAlex
- W4378945402
- Cited by
- 48
- References
- 69
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 13 · 2023: 2
- FWCI
- 4.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- IT University Of Copenhagen (IT University of Copenhagen) (acceptedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Archive of Doctoral Theses and Digital Collections (University of Calabria) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Carlsbergfondet (CF21-0432)
- PNRR FAIR (H23C22000860006, M4C21.3 spoke 9)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Social Sciences › Communication › Social Media and Politics
- Keywords
- Mastodon, social influence, collective behavior, coordinated behavior, information dissemination, compartmental epidemic model, network structure, social network density, online communities, identity, behavior change
- MeSH terms
- Animals, Communication, Humans, Mastodons, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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