The Effect of Phrase Vector Embedding in Explainable Hierarchical Attention-Based Tamil Code-Mixed Hate Speech and Intent Detection

Abstract

The substantial growth in the number of social media users has led to a significant increase in code-mixed content on social media platforms. Millions of users on these platforms upload pictures, videos, and post comments regarding their recent or interesting activities in their lives. In response to this uploaded content, some users occasionally employ offensive language to insult others or specific groups. Social media platforms encounter challenges in identifying and removing hate speech and objectionable content expressed in various languages. Hate speech, in its general sense, refers to harmful posts directed at individuals or groups based on factors such as their sexuality, religion, community affiliation, disability, and others. Typically, offensive language is directly or indirectly utilized in hate speech posts to insult someone, causing psychological distress to users. In light of this, we propose the development of a system designed to automatically block, remove, or report posts written in code-mixed Tamil that contain hate speech. We have gathered code-mixed Tamil comments from Twitter and the Helo App, categorizing them as hate speech and classifying their intent. We have identified three categories of hate speech intent, namely Targeted Individual (TI), Targeted Group (TG), and Others (O). The Targeted Individual (TI) class encompasses posts aimed at a specific individual target, while the Targeted Group (TG) category primarily focuses on identifying people based on their religion, community, gender, and other characteristics. The Others (O) category encompasses untargeted offensive posts and any other posts containing offensive language. In this context, we propose the use of a phrase-based, Explainable Hierarchical Attention model for hate speech detection. The results demonstrate that the proposed method is more effective in identifying and explaining hate speech and offensive language in social media posts.