{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:35:40Z","timestamp":1772120140601,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"European Union Horizon Europe Research and Innovation program","award":["101135437"],"award-info":[{"award-number":["101135437"]}]},{"name":"European Union Horizon Europe Research and Innovation program","award":["101135437"],"award-info":[{"award-number":["101135437"]}]},{"name":"European Union Horizon Europe Research and Innovation program","award":["101135437"],"award-info":[{"award-number":["101135437"]}]},{"name":"European Union Horizon Europe Research and Innovation program","award":["101135437"],"award-info":[{"award-number":["101135437"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soc. Netw. Anal. Min."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Social bots are a known problem in today\u2019s 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.<\/jats:p>","DOI":"10.1007\/s13278-025-01567-z","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T04:44:27Z","timestamp":1768365867000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bots into the Fediverse"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7883-4812","authenticated-orcid":false,"given":"Francisco","family":"Moreno","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8784-0907","authenticated-orcid":false,"given":"Pablo","family":"Perdomo-Quinteiro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2177-6185","authenticated-orcid":false,"given":"Gustavo","family":"Hernandez-Penaloza","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7400-9591","authenticated-orcid":false,"given":"Federico","family":"\u00c1lvarez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4009-2662","authenticated-orcid":false,"given":"Alberto","family":"Belmonte","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0695-4035","authenticated-orcid":false,"given":"Miguel Antonio","family":"Barbero-\u00c1lvarez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"issue":"6","key":"1567_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S41109-021-00392-5\/FIGURES\/10","volume":"12","author":"LL Cava","year":"2021","unstructured":"Cava LL, Greco S, Tagarelli A (2021) Understanding the growth of the Fediverse through the lens of Mastodon. Appl Netw Sci 12(6):1\u201335. https:\/\/doi.org\/10.1007\/S41109-021-00392-5\/FIGURES\/10","journal-title":"Appl Netw Sci"},{"key":"1567_CR2","doi-asserted-by":"crossref","unstructured":"Aimeur SE, Amri Brassard G (2023) Fake news, disinformation and misinformation in social media: a review. Soc Netw Anal. 13(11).","DOI":"10.1007\/s13278-023-01028-5"},{"key":"1567_CR3","doi-asserted-by":"publisher","unstructured":"Shao C, Ciampaglia GL, Varol O, Yang KC, Flammini A, Menczer F (2018) The spread of low-credibility content by social bots. Nat Commun. 9:1\u20139. https:\/\/doi.org\/10.1038\/s41467-018-06930-7","DOI":"10.1038\/s41467-018-06930-7"},{"key":"1567_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.tele.2023.102051","volume":"85","author":"H Luo","year":"2023","unstructured":"Luo H, Meng X, Zhao Y, Cai M (2023) Rise of social bots: the impact of social bots on public opinion dynamics in public health emergencies from an information ecology perspective. Telemat Inform 85:102051. https:\/\/doi.org\/10.1016\/j.tele.2023.102051","journal-title":"Telemat Inform"},{"issue":"9","key":"1567_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1057\/S41599-022-01174-9","volume":"12","author":"C Carrasco-Farr\u00e9","year":"2022","unstructured":"Carrasco-Farr\u00e9 C (2022) The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions. Humanit Soc Sci Commun 12(9):1\u201318. https:\/\/doi.org\/10.1057\/S41599-022-01174-9","journal-title":"Humanit Soc Sci Commun"},{"issue":"1","key":"1567_CR6","doi-asserted-by":"publisher","DOI":"10.2196\/17187","volume":"23","author":"V Suarez-Lledo","year":"2021","unstructured":"Suarez-Lledo V, Alvarez-Galvez J (2021) Prevalence of health misinformation on social media: systematic review. J Med Internet Res 23(1):e17187. https:\/\/doi.org\/10.2196\/17187","journal-title":"J Med Internet Res"},{"issue":"1","key":"1567_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.respol.2022.104628","volume":"52","author":"M Cantarella","year":"2023","unstructured":"Cantarella M, Fraccaroli N, Volpe R (2023) Does fake news affect voting behaviour? Res Policy 52(1):104628. https:\/\/doi.org\/10.1016\/j.respol.2022.104628","journal-title":"Res Policy"},{"key":"1567_CR8","doi-asserted-by":"publisher","unstructured":"Hanley HWA, Kumar D, Durumeric Z (2023) A Golden Age: Conspiracy Theories\u2019 Relationship with Misinformation Outlets, News Media, and the Wider Internet. Proc ACM Hum-Comput Interact. (CSCW2). https:\/\/doi.org\/10.1145\/3610043","DOI":"10.1145\/3610043"},{"key":"1567_CR9","doi-asserted-by":"publisher","DOI":"10.1177\/00113921211034896","author":"P Bleakley","year":"2021","unstructured":"Bleakley P (2021) Panic, pizza and mainstreaming the alt-right: a social media analysis of Pizzagate and the rise of the QAnon conspiracy. Curr Sociol. https:\/\/doi.org\/10.1177\/00113921211034896","journal-title":"Curr Sociol"},{"issue":"6","key":"1567_CR10","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1177\/08969205211049499","volume":"48","author":"RJ Antonio","year":"2022","unstructured":"Antonio RJ (2022) Democracy and capitalism in the interregnum: Trump\u2019s failed self-coup and after. Crit Sociol 48(6):937\u2013965","journal-title":"Crit Sociol"},{"key":"1567_CR11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.copsyc.2021.06.004","volume":"43","author":"LSD Beckes","year":"2021","unstructured":"Beckes LSD (2021) Social baseline theory: State of the science and new directions. Curr Opin Psychol 43:65\u201371. https:\/\/doi.org\/10.1016\/j.copsyc.2021.06.004","journal-title":"Curr Opin Psychol"},{"key":"1567_CR12","doi-asserted-by":"crossref","unstructured":"Bono CA, La\u00a0Cava L, Luceri L, Pierri F (2024) An Exploration of Decentralized Moderation on Mastodon. In Proceedings of the 16th ACM Web Science Conference. WEBSCI \u201924. New York, NY, USA: Association for Computing Machinery; p. 53\u201358","DOI":"10.1145\/3614419.3644016"},{"key":"1567_CR13","doi-asserted-by":"crossref","unstructured":"La\u00a0Cava L, Mandaglio D, Tagarelli A (2024) Polarization in Decentralized Online Social Networks. In Proceedings of the 16th ACM Web Science Conference. WEBSCI \u201924. New York, NY, USA: Association for Computing Machinery; p. 48\u201352","DOI":"10.1145\/3614419.3644013"},{"key":"1567_CR14","unstructured":"Zia HB, Raman A, Castro I, Tyson G (2025) Collaborative Content Moderation in the Fediverse. arXiv:2501.05871 [cs.SI]"},{"key":"1567_CR15","unstructured":"Alrubaian M, Al-Qurishi M, Omar S, Mostafa MA (2021) DeepTrust: A Deep Learning Approach for Measuring Social Media Users Trustworthiness. ArXiv.abs\/2101.07725"},{"key":"1567_CR16","unstructured":"Feng S, Tan Z, Li R, Luo M (2021) Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformers. In AAAI Conference on Artificial Intelligence; p. 1\u20138. Available from: https:\/\/api.semanticscholar.org\/CorpusID:237428699"},{"key":"1567_CR17","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2024.1509179","author":"G Tzoumanekas","year":"2024","unstructured":"Tzoumanekas G, Chatzianastasis M, Ilias L, Kiokes G, Psarras J, Askounis D (2024) A graph neural architecture search approach for identifying bots in social media. Front Artif Intell. https:\/\/doi.org\/10.3389\/frai.2024.1509179","journal-title":"Front Artif Intell"},{"key":"1567_CR18","doi-asserted-by":"crossref","unstructured":"Cresci S, Di\u00a0Pietro R, Petrocchi M, Spognardi A, Tesconi M (2017) The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race. In Proceedings of the 26th International Conference on World Wide Web Companion. WWW \u201917 Companion. Republic and Canton of Geneva, CHE: International World Wide Web Conferences Steering Committee; p. 963\u2013972","DOI":"10.1145\/3041021.3055135"},{"issue":"7","key":"1567_CR19","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1145\/2818717","volume":"59","author":"E Ferrara","year":"2016","unstructured":"Ferrara E, Varol O, Davis C, Menczer F, Flammini A (2016) The rise of social bots. Commun ACM 59(7):96\u2013104. https:\/\/doi.org\/10.1145\/2818717","journal-title":"Commun ACM"},{"issue":"6","key":"1567_CR20","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/MC.2016.183","volume":"49","author":"VS Subrahmanian","year":"2016","unstructured":"Subrahmanian VS, Azaria A, Durst S, Kagan V, Galstyan A, Lerman K et al (2016) The DARPA Twitter Bot Challenge. Computer 49(6):38\u201346. https:\/\/doi.org\/10.1109\/MC.2016.183","journal-title":"Computer"},{"key":"1567_CR21","doi-asserted-by":"publisher","first-page":"54591","DOI":"10.1109\/ACCESS.2021.3068659","volume":"9","author":"D Mart\u00edn-Guti\u00e9rrez","year":"2021","unstructured":"Mart\u00edn-Guti\u00e9rrez D, Hern\u00e1ndez-Pe\u00f1aloza G, Hern\u00e1ndez AB, Lozano-Diez A, \u00c1lvarez F (2021) A deep learning approach for Robust detection of Bots in Twitter using transformers. IEEE Access 9:54591\u201354601. https:\/\/doi.org\/10.1109\/ACCESS.2021.3068659","journal-title":"IEEE Access"},{"key":"1567_CR22","doi-asserted-by":"crossref","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). p. 4171\u20134186","DOI":"10.18653\/v1\/N19-1423"},{"key":"1567_CR23","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D et\u00a0al (2019) RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR. abs\/1907.11692. arXiv:1907.11692"},{"key":"1567_CR24","first-page":"445","volume-title":"User experience and behavior: 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15\u201320, 2018, Proceedings, Part I","author":"C Grimme","year":"2018","unstructured":"Grimme C, Assenmacher D, Adam L (2018) Changing perspectives: is it sufficient to detect social Bots? In: Computing S, Media S (eds) User experience and behavior: 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15\u201320, 2018, Proceedings, Part I. Springer-Verlag, Berlin, Heidelberg, pp 445\u2013461"},{"key":"1567_CR25","doi-asserted-by":"crossref","unstructured":"Varol O, Ferrara E, Davis C, Menczer F, Flammini A (2017) Online human-bot interactions: Detection, estimation, and characterization. In: Proceedings of the International AAAI Conference on Web and Social Media. AAAI Press; p. 280\u2013289. Available from: arxiv:1703.03107","DOI":"10.1609\/icwsm.v11i1.14871"},{"key":"1567_CR26","doi-asserted-by":"crossref","unstructured":"Feng S, Wan H, Wang N, Luo M (2022) BotRGCN: Twitter bot detection with relational graph convolutional networks. In Proceedings of the 2021 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining. ASONAM \u201921. Association for Computing Machinery, New York. p. 236\u2013239","DOI":"10.1145\/3487351.3488336"},{"key":"1567_CR27","unstructured":"He B, Yang Y, Wu Q, Liu H, Yang R, Peng H et\u00a0al (2024) Dynamicity-aware social bot detection with dynamic graph transformers. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. IJCAI \u201924; p. 5844\u20135852"},{"issue":"11","key":"1567_CR28","doi-asserted-by":"publisher","first-page":"3481","DOI":"10.3390\/s24113481","volume":"24","author":"X Wang","year":"2024","unstructured":"Wang X, Chen K, Wang K, Wang Z, Zheng K, Zhang J (2024) FedKG: a knowledge distillation-based federated graph method for Social Bot detection. Sensors 24(11):3481. https:\/\/doi.org\/10.3390\/s24113481","journal-title":"Sensors"},{"key":"1567_CR29","doi-asserted-by":"crossref","unstructured":"Yang Y, Yang R, Peng H, Li Y, Li T, Liao Y et\u00a0al (2023) FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection. In Proceedings of the ACM Web Conference 2023. WWW \u201923. Association for Computing Machinery, New York. p. 1314\u20131323","DOI":"10.1145\/3543507.3583500"},{"key":"1567_CR30","unstructured":"Pei H, Wei B, Chang KCC, Lei Y, Yang B (2020) Geom-GCN: Geometric Graph Convolutional Networks. In International Conference on Learning Representations (ICLR 2020); p. 1\u201310. Available from: https:\/\/openreview.net\/forum?id=S1e2agrFvS"},{"key":"1567_CR31","unstructured":"Zhu J, Yan Y, Zhao L, Heimann M, Akoglu L, Koutra D (2020) Beyond homophily in graph neural networks: current limitations and effective designs. In Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS \u201920. Curran Associates Inc., Red Hook. p. 7793\u20137804"},{"key":"1567_CR32","doi-asserted-by":"crossref","unstructured":"He B, Jiang X, Wu Q, Liu H, Yang Y, Liao Y (2025) Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. KDD \u201925. Association for Computing Machinery, New York. p. 860\u2013871","DOI":"10.1145\/3711896.3736862"},{"key":"1567_CR33","doi-asserted-by":"crossref","unstructured":"Mannocci L, Cresci S, Monreale A, Vakali A,Tesconi M (2022) Unsupervised Bot detection based on multivariate time series, MulBot. pp 1485\u20131494","DOI":"10.1109\/BigData55660.2022.10020363"},{"key":"1567_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113019","volume":"311","author":"G Lingam","year":"2025","unstructured":"Lingam G, Das SK (2025) Social bot detection using variational generative adversarial networks with hidden Markov models in Twitter network. Knowl-Based Syst 311:113019. https:\/\/doi.org\/10.1016\/j.knosys.2025.113019","journal-title":"Knowl-Based Syst"},{"key":"1567_CR35","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201916. ACM; p. 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"issue":"3","key":"1567_CR36","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1145\/175247.175256","volume":"37","author":"DE Rumelhart","year":"1994","unstructured":"Rumelhart DE, Widrow B, Lehr MA (1994) The basic ideas in neural networks. Commun ACM 37(3):87\u201393","journal-title":"Commun ACM"},{"issue":"35","key":"1567_CR37","doi-asserted-by":"publisher","first-page":"6679","DOI":"10.1609\/AAAI.V35I8.16826","volume":"5","author":"S Ar\u0131k","year":"2021","unstructured":"Ar\u0131k S, Pfister T (2021) TabNet: attentive interpretable tabular learning. Proc AAAI Conf Artif Intell 5(35):6679\u20136687. https:\/\/doi.org\/10.1609\/AAAI.V35I8.16826","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1567_CR38","doi-asserted-by":"crossref","unstructured":"Lingam G, Yasaswini B, Jagadamba PVSL, Kolliboyana N (2022) An Improved Bot Identification with Imbalanced Data using GG-XGBoost. In 2022 2nd International Conference on Intelligent Technologies (CONIT). p. 1\u20136","DOI":"10.1109\/CONIT55038.2022.9848252"},{"issue":"86","key":"1567_CR39","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(86):2579\u20132605","journal-title":"J Mach Learn Res"},{"issue":"2","key":"1567_CR40","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TCSS.2021.3103515","volume":"9","author":"T Khaund","year":"2022","unstructured":"Khaund T, Kirdemir B, Agarwal N, Liu H, Morstatter F (2022) Social Bots and their coordination during online campaigns: a survey. IEEE Trans Comput Soc Syst 9(2):530\u2013545. https:\/\/doi.org\/10.1109\/TCSS.2021.3103515","journal-title":"IEEE Trans Comput Soc Syst"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13278-025-01567-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-025-01567-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-025-01567-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:39:33Z","timestamp":1769585973000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13278-025-01567-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,14]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1567"],"URL":"https:\/\/doi.org\/10.1007\/s13278-025-01567-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-7600191\/v1","asserted-by":"object"}]},"ISSN":["1869-5469"],"issn-type":[{"value":"1869-5469","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,14]]},"assertion":[{"value":"12 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"27"}}