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Unsupervised Machine Learning for Bot Detection on Twitter : Generating and Selecting Features for Accurate Clustering
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MAP20240013288
Al-azawi, Raad ‌
Unsupervised Machine Learning for Bot Detection on Twitter : Generating and Selecting Features for Accurate Clustering / by Raad Al-azawi and Safaa O. AL-mamory
Sumario: Twitter is a popular social media platform that is widely used by individuals and businesses. However, it is vulnerable to bot attacks, which can have negative effects on society. Supervised machine learning techniques can detect bots but require labeled data to differentiate between human and bot users. Twitter generates a significant amount of unlabeled data, which can be expensive to label. Unsupervised machine learning techniques, specifically clustering algorithms, are crucial for managing this data and reducing computational complexity
En: Revista Iberoamericana de Inteligencia Artificial. - : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018- = ISSN 1988-3064. - 19/06/2024 Volumen 27 Número 73 - junio 2024 , p.142-148
1. Redes sociales ‌. 2. Algoritmos ‌. 3. Ciberataques ‌. 4. Robots ‌. 5. Inteligencia artificial ‌. I. Título.