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Explainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Elections

Shevtsov Alexander, Tzagkarakis Christos, Antonakaki Despoina, Ioannidis Sotirios

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URIhttp://purl.tuc.gr/dl/dias/ACFF4C2F-785F-497F-A9DC-43FBC128AA4A-
Identifierhttps://doi.org/10.1016/j.simpa.2022.100333-
Identifierhttps://www.sciencedirect.com/science/article/pii/S2665963822000598-
Languageen-
Extent2 pagesen
TitleExplainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Electionsen
CreatorShevtsov Alexanderen
CreatorTzagkarakis Christosen
CreatorAntonakaki Despoinaen
CreatorIoannidis Sotiriosen
CreatorΙωαννιδης Σωτηριοςel
PublisherElsevieren
DescriptionThis document is the result of the research projects CONCORDIA (grant number 830927), CyberSANE (grant number 833683) and PUZZLE (grant number 883540) co-funded by the European Commission, with (EUROPEAN COMMISSION Directorate-General Communications Networks, Content and Technology).en
DescriptionOriginal software publicationen
Content SummaryThis study introduces a novel, reproducible and reusable Twitter bot identification system. The system uses a machine learning (ML) pipeline, fed with hundreds of features extracted from a Twitter corpus. The main objective of the proposed ML pipeline is to train and validate different state-of-the-art machine learning models, where the eXtreme Gradient Boosting (XGBoost) model is selected since it achieves the highest detection performance. The Twitter dataset was collected during the 2020 US Presidential Elections, and additional experimental evaluation on distinct Twitter datasets demonstrates the superiority of our approach, in terms of high bot detection accuracy.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2024-01-08-
Date of Publication2022-
SubjectMachine learningen
SubjectTwitter bot detectionen
SubjectModel explainabilityen
Bibliographic CitationA. Shevtsov, C. Tzagkarakis, D. Antonakaki, and S. Ioannidis, “Explainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Elections,” Software Impacts, vol. 13, Aug. 2022, doi: 10.1016/j.simpa.2022.100333.en

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