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

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      <subfield code="a">Unsupervised Machine Learning for Bot Detection on Twitter</subfield>
      <subfield code="b">: Generating and Selecting Features for Accurate Clustering</subfield>
      <subfield code="c">by Raad Al-azawi and Safaa O. AL-mamory</subfield>
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      <subfield code="a">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</subfield>
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      <subfield code="g">19/06/2024 Volumen 27 Número 73 - junio 2024 , p.142-148</subfield>
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      <subfield code="t">Revista Iberoamericana de Inteligencia Artificial</subfield>
      <subfield code="d"> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</subfield>
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