Detecting offensive speech in conversational code-mixed dialogue on social media: A contextual dataset and benchmark experiments

Abstract

The spread of Hate Speech on online platforms is a severe issue for societies and requires the identification of offensive content by platforms. Research has modeled Hate Speech recognition as a�text classification�problem that predicts the class of a message based on the text of the message only. However, context plays a huge role in communication. In particular, for short messages, the text of the preceding tweets can completely change the interpretation of a message within a discourse. This work extends previous efforts to classify Hate Speech by considering the current and previous tweets jointly. In particular, we introduce a clearly defined way of extracting context. We present the development of the first dataset for conversational-based Hate Speech classification with an approach for collecting context from long conversations for code-mixed Hindi (ICHCL dataset). Overall, our benchmark experiments show that the inclusion of context can improve classification performance over a baseline. Furthermore, we develop a novel processing pipeline for processing the context. The best-performing pipeline uses a fine-tuned SentBERT paired with an�LSTM�as a classifier. This pipeline achieves a macro F1 score of 0.892 on the ICHCL test dataset. Another�KNN, SentBERT, and ABC weighting-based pipeline yields an F1 Macro of 0.807, which gives the best results among traditional classifiers. So even a KNN model gives better results with an optimized�BERT�than a vanilla BERT model.