Bots into the Fediverse
Year: 2026
Type: article
Abstract: Social bots are a known problem in today’s society. They are influenced by a variety of factors, ranging from the presence of bots to a lack of interaction between bots and users. This paper proposes a cross-platform approach for the detection of social bots based on profile metadata and text embeddings, applied to Twitter, Mastodon, and Bluesky user accounts. The resulting model achieves 97.39% accuracy in a four-class classification task, outperforming several established baselines, including graph-based and federated approaches while being computationally efficient. The primary contribution of this work is the demonstration that user features can support effective bot classification across heterogeneous and decentralized environments, demonstrating the feasibility of cross-domain generalization at scale. We additionally present a novel dataset that combines self-identified bot and non-bot accounts from decentralized platforms.
Institution: Universidad Politécnica de Madrid
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
Funder: European Commission
OpenAlex ID: W7123442882