2021
DOI: 10.1016/j.procs.2021.01.207
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Accurate Cyberbullying Detection and Prevention on Social Media

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Cited by 83 publications
(27 citation statements)
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“…Prior work around toxicity understanding mainly focuses on detection (Schmidt and Wiegand, 2017). Early approaches include using n-grams (Waseem and Hovy, 2016;Sood et al, 2012;Perera and Fernando, 2021) as well as word clustering (Xiang et al, 2012;Zhong et al, 2016). Recently, knowledge enhanced approaches have also been used for toxicity detection.…”
Section: Toxic Text Understandingmentioning
confidence: 99%
“…Prior work around toxicity understanding mainly focuses on detection (Schmidt and Wiegand, 2017). Early approaches include using n-grams (Waseem and Hovy, 2016;Sood et al, 2012;Perera and Fernando, 2021) as well as word clustering (Xiang et al, 2012;Zhong et al, 2016). Recently, knowledge enhanced approaches have also been used for toxicity detection.…”
Section: Toxic Text Understandingmentioning
confidence: 99%
“…Researchers initially applied the bag-of-words approach, part-of-speech tagging, n-gram features, or a combination thereof for feature detection (Dinakar et al, 2011). Most recent studies have focused on content-based features, such as lexical, syntactic, and sentiment information; findings have demonstrated the importance of these words in the automatic detection of cyberbullying (Ptaszynski et al, 2016;Zhao et al, 2016;Zhao and Mao, 2017;Perera and Fernando, 2021).…”
Section: Cyberbullyingmentioning
confidence: 99%
“…SVM achieved 77.65% accuracy, 58% recall and 70% precision. Perera and Fernando [52] adopted supervised machine learning to detect cyberbullying, specifically by using labelled data set from Twitter. They extracted TF-IDF, sentiment analysis, profanity and pronoun to be fed into SVM as classifier in their research.…”
Section: Cyberbullying Detection Using Machine Learningmentioning
confidence: 99%