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- W4387272168 abstract "Twitter provides a rich field for open human conversation, yet it also attracts many fully automated or partially automated accounts disguised as human users. These accounts mostly encourage criminal actions, including manipulating ideas and disseminating abusive speech, to mention a few. The malicious information spreads in online discussions, particularly during election seasons, where, aside from lawful bots employed for propagation and communication, the intention is to influence public sentiment and the voters towards a specific direction, philosophy, or political group. Despite implementing various AI-driven bot detection technologies, the basis for bot categorization and generalization remains murky, and AI judgments are deprived of ethical accountability. Most of these techniques use black-boxed algorithms, and their efficacy is frequently questioned. The purpose of this study is to present the creation of a new approach for recognizing Twitter bots using annotated Twitter data. To that purpose, an explainable AI-based machine learning (ML) approach is used, with the hyper-parameters tweaked through cross-validation. Shapley Additive Explanations (SHAP) are also used in our work to explain ML model predictions by assessing feature significance using game theory Shapley values. Experiments show that our proposed model provides excellent results with an accuracy of 87.9% and an AUC-ROC value of 0.94 for the test set." @default.
- W4387272168 created "2023-10-03" @default.
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- W4387272168 date "2023-01-01" @default.
- W4387272168 modified "2023-10-03" @default.
- W4387272168 title "Explainable Twitter bot detection model for limited features" @default.
- W4387272168 doi "https://doi.org/10.1049/icp.2023.1822" @default.
- W4387272168 hasPublicationYear "2023" @default.
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