Thèses en ligne de l'université 8 Mai 1945 Guelma

Enhancing Social Network Security: Machine Learning-Based Bot Detection

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dc.contributor.author TALHA, ZIED
dc.date.accessioned 2024-12-02T12:45:13Z
dc.date.available 2024-12-02T12:45:13Z
dc.date.issued 2024
dc.identifier.uri http://dspace.univ-guelma.dz/jspui/handle/123456789/16472
dc.description.abstract The proliferation of social media platforms has transformed communication, but it has also given rise to social media bots that can spread misinformation, manipulate public opinion, and compromise the integrity of online discourse. This thesis addresses the critical issue of detecting social media bots on Twitter. Traditional detection methods often fall short due to the evolving nature of these bots and the vast amount of data involved. To overcome these challenges, this research proposes a hybrid ensemble model that combines profile-based and content-based features with advanced natural language processing techniques. This approach captures a wide range of bot behaviors and characteristics, resulting in more accurate and robust detection. The thesis includes an examination of social media platforms and the threats posed by bots, a review of current bot detection methods, an in-depth explanation of the proposed hybrid ensemble methodology, and an experimental evaluation of the methodology’s effectiveness compared to leading techniques. The findings demonstrate significant improvements in detection performance, supporting efforts to protect social media environments from harmful automated entities. en_US
dc.language.iso en en_US
dc.publisher University of Guelma en_US
dc.subject Social media, Bots, Detection, Twitter, Machine learning, Natural language processing, Hybrid model. en_US
dc.title Enhancing Social Network Security: Machine Learning-Based Bot Detection en_US
dc.type Working Paper en_US


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