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.

研究了在 Dravidian 语言中检测社交媒体评论中的 LGBT-phobia 内容，并应用卷积神经网络、LSTM、BERT 等深度学习模型进行分类。结果表明 IndicBERT 模型在 Malayalam 和 Tamil 语言中的加权平均 F1-score 分别为0.86 和0.77，具有更高的分类性能。

探索使用深度学习方法探测德拉维达语中的恐同和跨性别歧视