Unsupervised Machine Learning for Bot Detection on Twitter : Generating and Selecting Features for Accurate Clustering
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20240013288 | ||
| 003 | MAP | ||
| 005 | 20240829122056.0 | ||
| 008 | 240829e20240619esp|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a922.13 | ||
| 100 | 1 | $0MAPA20220004824$aAl-azawi, Raad | |
| 245 | 1 | 0 | $aUnsupervised Machine Learning for Bot Detection on Twitter$b: Generating and Selecting Features for Accurate Clustering$cby Raad Al-azawi and Safaa O. AL-mamory |
| 520 | $aTwitter 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 | ||
| 650 | 4 | $0MAPA20080570286$aRedes sociales | |
| 650 | 4 | $0MAPA20080553128$aAlgoritmos | |
| 650 | 4 | $0MAPA20140023066$aCiberataques | |
| 650 | 4 | $0MAPA20080542245$aRobots | |
| 650 | 4 | $0MAPA20080611200$aInteligencia artificial | |
| 773 | 0 | $wMAP20200034445$g19/06/2024 Volumen 27 Número 73 - junio 2024 , p.142-148$x1988-3064$tRevista Iberoamericana de Inteligencia Artificial$d : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018- | |
| 856 | $uhttps://journal.iberamia.org/index.php/intartif/article/view/1119 |