Unified Graph Convolutional Neural Network with PPMI-Informed Graphs and MuRIL Embeddings for Hate Speech Detection in Indian Code-Mix Text

The widespread use of social media has led to a rise in online bullying and abusive language. Huge amounts of content are created on social media daily, creating a challenge for platforms to manually detect and remove offensive posts, making automated detection systems essential. This challenge is even greater in multilingual countries such as India, where code mixing is prevalent. This paper investigates the effectiveness of graph-based representations for hate speech and offensive (HOS) language detection in social media posts written in code-mixed low-resource languages. We conducted experiments on seven openly available datasets spanning Kannada-English, Malayalam-English, Tamil-English, and Hindi-English code-mixed languages. The main goal is to identify the most effective edge-weight computation method for hate speech detection across the target languages by conducting a comprehensive comparative analysis of different edge construction techniques. We construct sentence graphs with MuRIL word embeddings as nodes and compare three edge-weighting schemes—Positive Pointwise Mutual Information (PPMI), Sequence Graph Transform (SGT), and raw co-occurrence processed through a Graph Convolutional Network (GCN)—concatenated with a MuRIL sentence embedding for classification. The highest test-set accuracies and the corresponding best weighted F1 results obtained for PPMI were 82.00%, 73.89% for HASOC Malayalam 2020, 65.26%, 65.81% for HASOC Malayalam 2021, 75.37%, 75.37% for HASOC Tamil, 72.7%, 72.68% for HASOC Hindi-English, 63.36%, 55.58% for EACL Kannada, 92.05%, 89.94% for EACL Malayalam, and 73.84%, 63.62% for EACL Tamil. Further, we conducted a comprehensive analysis of sentence representation quality and model performance and provided detailed mathematical and empirical analysis of the different graph representations.