CIT Kokrajhar Team: LSTM based Deep RNN Architecture for Hate Speech and Offensive Content (HASOC) Identification in Indo-European Languages

Recently, automated hate speech and offensive content identification has received significant attention due to rapid propagation of cyberbullying which undermines objective discussions in social media and adversely affects the outcome of the online social democratic processes. A special type of Recurrent Neural Network (RNN) based deep learning approach called Long Short Term Memory (LSTM) is implemented for automatic hate speech and offensvie content identification. Separating offensive content is quite challenging because the abusive language is quite subjective in nature and highly context dependent. This paper offers language-agnostic solution in three Indo-European languages (English, German, and Hindi) since no pre-trained word embedding is used. Experimental results offer very attractive insights.