Detection of Homophobia & Transphobia in Dravidian Languages: Exploring Deep Learning Methods
This addresses the problem of toxic online content targeting the LGBT+ community in under-resourced languages, though it is incremental as it applies existing models to new language data.
The paper tackled automated detection of homophobic and transphobic content in low-resource Dravidian languages (Malayalam and Tamil) by applying deep learning models including CNN, LSTM, and transformer-based approaches, finding that IndicBERT achieved the best performance with weighted average F1-scores of 0.86 for Malayalam and 0.77 for Tamil.
The increase in abusive content on online social media platforms is impacting the social life of online users. Use of offensive and hate speech has been making so-cial media toxic. Homophobia and transphobia constitute offensive comments against LGBT+ community. It becomes imperative to detect and handle these comments, to timely flag or issue a warning to users indulging in such behaviour. However, automated detection of such content is a challenging task, more so in Dravidian languages which are identified as low resource languages. Motivated by this, the paper attempts to explore applicability of different deep learning mod-els for classification of the social media comments in Malayalam and Tamil lan-guages as homophobic, transphobic and non-anti-LGBT+content. The popularly used deep learning models- Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) using GloVe embedding and transformer-based learning models (Multilingual BERT and IndicBERT) are applied to the classification problem. Results obtained show that IndicBERT outperforms the other imple-mented models, with obtained weighted average F1-score of 0.86 and 0.77 for Malayalam and Tamil, respectively. Therefore, the present work confirms higher performance of IndicBERT on the given task in selected Dravidian languages.