  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. Detection of hate speech on social media
websites faces a variety of challenges including small imbalanced data sets,
the findings of an appropriate model and also the choice of feature analysis
method. further more, this problem is more severe for the Bengali speaking
community due to the lack of gold standard labelled datasets. This paper
presents a new dataset of 30,000 user comments tagged by crowd sourcing and
varified by experts. All the comments are collected from YouTube and Facebook
comment section and classified into seven categories: sports, entertainment,
religion, politics, crime, celebrity and TikTok & meme. A total of 50
annotators annotated each comment three times and the majority vote was taken
as the final annotation. Nevertheless, we have conducted base line experiments
and several deep learning models along with extensive pre-trained Bengali word
embedding such as Word2Vec, FastText and BengFastText on this dataset to
facilitate future research opportunities. The experiment illustrated that
although all deep learning models performed well, SVM achieved the best result
with 87.5% accuracy. Our core contribution is to make this benchmark dataset
available and accessible to facilitate further research in the field of in the
field of Bengali hate speech detection.
