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Bengali social media hate speech detectionĪwal MA, Rahman MS, Rabbi J (2018) Detecting abusive comments in discussion threads using Naïve Bayes.Our core contribution is to make this benchmark dataset available and accessible to facilitate further research in the field of Bengali hate speech detection. The experiment illustrated that although all the deep learning model performed well, SVM achieved the best result with 87.5% accuracy. Nevertheless, we have conducted baseline experiments and several deep learning models along with extensive pretrained Bengali word embedding such as Word2Vec, FastText and BengFastText on this dataset to facilitate future research opportunities. A total of 50 annotators annotated each comment three times, and the majority vote was taken as the final annotation. All the user comments collected from YouTube and Facebook comment section and classified into seven categories: sports, entertainment, religion, politics, crime, celebrity and TikTok and meme.
This paper presents a new dataset of 30,000 user comments tagged by crowdsourcing and verified by expert. Furthermore, this problem is more severe for the Bengali speaking community due to the lack of gold standard labeled datasets. Detection of hate speech on social media Web sites faces a variety of challenges including small imbalanced datasets, the finding of an appropriate model and also the choice of feature analysis method. Social media sites such as YouTube and Facebook have become an integral part of everyone’s life, and in the last few years, hate speech in the social media comment section has increased rapidly.
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