Analyzing Toxic Behavior and Its Impact on the Mastodon Community

Abstract:Mastodon as a decentralized federation of independently moderated social servers poses unique challenges for the detection and mitigation of toxic content. There are no unified moderation standards. The ecosystem is very diverse and uneven. This paper explores the development and spread of toxicity in Mastodon, utilizing machine learning methods to examine user posts. The results offer clarity on toxicity trends and its implications for community health and decentralized governance.

Computation and Language (cs.CL); Computational Engineering, Finance, and Science (cs.CE)

arXiv:2607.21980 [cs.CL]

(or arXiv:2607.21980v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.21980 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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