Explainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential ElectionsExplainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Elections Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2024-01-082022enThis 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.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).Original software publicationhttp://creativecommons.org/licenses/by/4.0/Software Impacts13Shevtsov_et_al_Software Impacts_13_2022.pdfChania [Greece]Library of TUC2024-01-08application/pdf348.8 kBfree Shevtsov Alexander Tzagkarakis Christos Antonakaki Despoina Ioannidis Sotirios Ιωαννιδης Σωτηριος Elsevier Machine learning Twitter bot detection Model explainability