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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

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/ACFF4C2F-785F-497F-A9DC-43FBC128AA4A-
Αναγνωριστικόhttps://doi.org/10.1016/j.simpa.2022.100333-
Αναγνωριστικόhttps://www.sciencedirect.com/science/article/pii/S2665963822000598-
Γλώσσαen-
Μέγεθος2 pagesen
ΤίτλοςExplainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Electionsen
ΔημιουργόςShevtsov Alexanderen
ΔημιουργόςTzagkarakis Christosen
ΔημιουργόςAntonakaki Despoinaen
ΔημιουργόςIoannidis Sotiriosen
ΔημιουργόςΙωαννιδης Σωτηριοςel
ΕκδότηςElsevieren
ΠεριγραφήThis 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
ΠεριγραφήOriginal software publicationen
ΠερίληψηThis 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2024-01-08-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαMachine learningen
Θεματική ΚατηγορίαTwitter bot detectionen
Θεματική ΚατηγορίαModel explainabilityen
Βιβλιογραφική ΑναφοράA. 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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