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| URI | http://purl.tuc.gr/dl/dias/ACFF4C2F-785F-497F-A9DC-43FBC128AA4A | - |
| Αναγνωριστικό | https://doi.org/10.1016/j.simpa.2022.100333 | - |
| Αναγνωριστικό | https://www.sciencedirect.com/science/article/pii/S2665963822000598 | - |
| Γλώσσα | en | - |
| Μέγεθος | 2 pages | en |
| Τίτλος | Explainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Elections | en |
| Δημιουργός | Shevtsov Alexander | en |
| Δημιουργός | Tzagkarakis Christos | en |
| Δημιουργός | Antonakaki Despoina | en |
| Δημιουργός | Ioannidis Sotirios | en |
| Δημιουργός | Ιωαννιδης Σωτηριος | el |
| Εκδότης | Elsevier | en |
| Περιγραφή | This 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 |
| Περιγραφή | Original software publication | en |
| Περίληψη | This 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 |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2024-01-08 | - |
| Ημερομηνία Δημοσίευσης | 2022 | - |
| Θεματική Κατηγορία | Machine learning | en |
| Θεματική Κατηγορία | Twitter bot detection | en |
| Θεματική Κατηγορία | Model explainability | en |
| Βιβλιογραφική Αναφορά | A. 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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