Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection

By Hajime Watanabe, Mondher Bouazizi, Tomoaki Ohtsuki. IEEE Access, 2018

362 citations recorded by the source corpus.

With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds has become more direct, resulting in more and more “cyber” conflicts between these people. Consequently, hate speech is used more and more, to the point where it has become a serious problem invading these open spaces. Hate speech refers to the use of aggressive, violent or offensive language, targeting a specific group of people sharing a common property, whether this property is their gender (i.e., sexism), their ethnic group or race (i.e., racism) or their believes and religion. While most of the online social networks and microblogging websites forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content. Therefore, arises the necessity to detect such speech automatically and filter any content that presents hateful language or language inciting to hatred. In this paper, we propose an approach to detect hate expressions on Twitter. Our approach is based on unigrams and patterns that are automatically collected from the training set. These patterns and unigrams are later used, among others, as features to train a machine learning algorithm. Our experiments on a test set composed of 2010 tweets show that our approach reaches an accuracy equal to 87.4% on detecting whether a tweet is offensive or not (binary classification), and an accuracy equal to 78.4% on detecting whether a tweet is hateful, offensive, or clean (ternary classification).

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/fd5b571c2c9065c855a3b72ca4eefcc83c29e9fb

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Citations in the corpus

Hate Speech Dataset from a White Supremacy Forum (2018, background): “Other proposals focus on the annotation of hate speech as opposed to texts containing derogatory or offensive language (Davidson et al., 2017; Malmasi and Zampieri, 2017, 2018; Watanabe et al., 2018).”
CARER: Contextualized Affect Representations for Emotion Recognition (2018, background): “Other advantages of the graph approach are that minimum domain knowledge and manual effort are required to capture important contextual and latent information, which are useful to disam-biguate meaning in emotional expressions.”