Classifying racist texts using a support vector machine

By Edel Greevy, Alan F. Smeaton. 2004

164 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018, background): “Besides traditional binary classification tasks, related work considers different aspects of toxic language, such as “racism” (Greevy and Smeaton, 2004; Waseem, 2016; Kwok and Wang, 2013) and “sexism” (Waseem and Hovy, 2016; Jha and Mamidi, 2017), or the severity of toxicity (Davidson et al.”
Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making (2015, methodology): “Specifically focusing on hateful and/or antagonistic content, Greevy and Smeaton (2004) classified racist content in Web pages using a supervised machine learning approach with a bag-of-words (BoW) as features.”
Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter (2018, methodology): “Among these, Support Vector Machines (SVM) is the most popular algorithm [3, 5, 9, 15, 23, 35, 38, 39], while other algorithms such as Naive Bayes [5, 9, 19, 23, 39], Logistic Regression [9, 11, 23, 36, 37], and Random Forest [9, 38] are also used.”
Characterizing and Detecting Hateful Users on Twitter (2018, background): “…content from tweets, posts and websites has been represented as n-grams, BoWs (Waseem and Hovy 2016; Kwok and Wang 2013; Magu, Joshi, and Luo 2017; Greevy and Smeaton 2004; Gitari et al. 2015), and word embeddings such as paragraph2vec (Djuric et al. 2015), GloVe (Pennington, Socher, and Manning…”
Benchmarking Aggression Identification in Social Media (2018, background): “…et al., 2017; Vigna et al., 2017; Djuric et al., 2015; Fortana, 2017; Gitari et al., 2015; Malmasi and Zampieri, 2018; Waseem and Hovy, 2016; Schmidt and Wie-gand, 2017), radicalization (Agarwal and Sureka, 2015; Agarwal and Sureka, 2017), racism (Greevy and Smeaton, 2004; Greevy, 2004) and others.”
A Survey on Automatic Detection of Hate Speech in Text (2018, methodology): “Another model similar to dictionaries is the bag-of-words [9, 33, 50].”
A Measurement Study of Hate Speech in Social Media (2017, background): “Back in 2004, there has been efforts that attempt to identify hateful webpages, containing racism or extremism [13].”