Unsupervised Machine Learning for Bot Detection on Twitter : Generating and Selecting Features for Accurate Clustering

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<title>Unsupervised Machine Learning for Bot Detection on Twitter</title>
<subTitle>: Generating and Selecting Features for Accurate Clustering</subTitle>
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<namePart>Al-azawi, Raad</namePart>
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<abstract displayLabel="Summary">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</abstract>
<note type="statement of responsibility">by Raad Al-azawi and Safaa O. AL-mamory</note>
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<topic>Redes sociales</topic>
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<topic>Robots</topic>
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<topic>Inteligencia artificial</topic>
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<url displayLabel="electronic resource" usage="primary display">https://journal.iberamia.org/index.php/intartif/article/view/1119</url>
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<title>Revista Iberoamericana de Inteligencia Artificial</title>
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<publisher> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</publisher>
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<identifier type="issn">1988-3064</identifier>
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<text>19/06/2024 Volumen 27 Número 73 - junio 2024 , p.142-148</text>
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