Investigating Deep Learning Approaches for Hate Speech Detection in Social Media
This addresses the problem of identifying hate speech in user-generated content like tweets, which is crucial for mitigating cyber crimes and social threats, but it appears incremental in method.
The paper tackled hate speech detection in social media by proposing deep learning approaches with various embeddings, achieving significant improvements in accuracy and F1-score on three publicly available datasets.
The phenomenal growth on the internet has helped in empowering individual's expressions, but the misuse of freedom of expression has also led to the increase of various cyber crimes and anti-social activities. Hate speech is one such issue that needs to be addressed very seriously as otherwise, this could pose threats to the integrity of the social fabrics. In this paper, we proposed deep learning approaches utilizing various embeddings for detecting various types of hate speeches in social media. Detecting hate speech from a large volume of text, especially tweets which contains limited contextual information also poses several practical challenges. Moreover, the varieties in user-generated data and the presence of various forms of hate speech makes it very challenging to identify the degree and intention of the message. Our experiments on three publicly available datasets of different domains shows a significant improvement in accuracy and F1-score.