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

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...

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Vydáno v:IEEE access Ročník 6; s. 13825 - 13835
Hlavní autoři: Watanabe, Hajime, Bouazizi, Mondher, Ohtsuki, Tomoaki
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Classification
Feature extraction
Hate speech
Hate websites
Machine learning
Minority & ethnic groups
Open spaces
Racism
Sentiment analysis
Social networks
Speech
Task analysis
Twitter
Voice activity detection
Web sites
Websites
ISSN:2169-3536, 2169-3536
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Shrnutí: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).
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2806394