Social media is bustling with ever growing cases of trolling, aggression and hate. A huge amount of social media data is generated each day which is insurmountable for manual inspection. In this work, we propose an efficient and fast method to detect aggression and misogyny in social media texts. We use data from the Second Workshop on Trolling, Aggression and Cyber Bullying for our task. We employ a BERT based model to augment our data. Next we employ Tf-Idf and XGBoost for detecting aggression and misogyny. Our model achieves 0.73 and 0.85 Weighted F1 Scores on the 2 prediction tasks, which are comparable to the state of the art. However, the training time, model size and resource requirements of our model are drastically lower compared to the state of the art models, making our model useful for fast inference.

In today’s world, online activity and social media are facing an upsurge of cases of aggression, gender-biased comments and communal hate. In this shared task, we used a CNN-LSTM hybrid method to detect aggression, misogynistic and communally charged content in social media texts. First, we employ text cleaning and convert the text into word embeddings. Next we proceed to our CNN-LSTM based model to predict the nature of the text. Our model achieves 0.288, 0.279, 0.294 and 0.335 Overall Micro F1 Scores in multilingual, Meitei, Bengali and Hindi datasets, respectively, on the 3 prediction labels.