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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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| URI | http://purl.tuc.gr/dl/dias/ACFF4C2F-785F-497F-A9DC-43FBC128AA4A | - |
| Identifier | https://doi.org/10.1016/j.simpa.2022.100333 | - |
| Identifier | https://www.sciencedirect.com/science/article/pii/S2665963822000598 | - |
| Language | en | - |
| Extent | 2 pages | en |
| Title | Explainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Elections | en |
| Creator | Shevtsov Alexander | en |
| Creator | Tzagkarakis Christos | en |
| Creator | Antonakaki Despoina | en |
| Creator | Ioannidis Sotirios | en |
| Creator | Ιωαννιδης Σωτηριος | el |
| Publisher | Elsevier | en |
| Description | 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 |
| Description | Original software publication | en |
| Content Summary | 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 |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2024-01-08 | - |
| Date of Publication | 2022 | - |
| Subject | Machine learning | en |
| Subject | Twitter bot detection | en |
| Subject | Model explainability | en |
| Bibliographic Citation | 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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