Investigating Deep Learning Approaches for Hate Speech Detection in Social Media

The phenomenal growth on the internet has helped in empowering individual's\nexpressions, but the misuse of freedom of expression has also led to the\nincrease of various cyber crimes and anti-social activities. Hate speech is one\nsuch issue that needs to be addressed very seriously as otherwise, this could\npose threats to the integrity of the social fabrics.\n In this paper, we proposed deep learning approaches utilizing various\nembeddings for detecting various types of hate speeches in social media.\nDetecting hate speech from a large volume of text, especially tweets which\ncontains limited contextual information also poses several practical\nchallenges.\n Moreover, the varieties in user-generated data and the presence of various\nforms of hate speech makes it very challenging to identify the degree and\nintention of the message. Our experiments on three publicly available datasets\nof different domains shows a significant improvement in accuracy and F1-score.\n