Sdutta at C om MA @ ICON : A CNN - LSTM Model for Hate Detection (2021)

Abstract: 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.

## introduction identifying aggressive and abusive atrocities on the internet is an important field of study in today's world. researchers are striving to develop remedial measures to combat such online content. in order to efficiently carry out these tasks, the research community have proposed several machine learning models, to enhance the efficiency of handling large sets of data and accurately assessing them. the extent of accuracy, however, is a point of concern, since ml models are entirely dependent on large, comprehensive training datasets. models are prone to poor performance due to lack of properly curated datasets. conventional models and ensembles are more reliable in these cases, as their data is easily interpreted. the work is designed to identify objectionable and abusive content on online platforms, as either aggressive, gender based or communally charged. the objective of the model is to demarcate the overlapping aspects of the three types of contents being investigated, and also if this intersectionality could be useful to the task. the task includes multilingual datasets to widen the spectrum of potentially abusive content and to challenge the models. ## related works important research contributions have been made in the domain of aggression detection in text @xcite @xcite and offensive language @xcite . gender bias and communally charged content detection have been investigated in research work such as @xcite , @xcite and @xcite respectively. aforementioned works are different in terms of the target subject they investigate. the nlp research fraternity has analysed the pragmatic and structural features of such forms of hate speech @xcite and developing systems that could automatically detect and handle these @xcite . although the most prevalent language for predicting model datasets is english, there are some other languages on which works have been reported, for example, in hindi @xcite . however, on a general note, any predictive model built on historical data may inadvertently inherit human biases based on gender or ethnicity @xcite @xcite . ## model description the prediction pipeline is described in figure (1 ). the task required us to detect aggression, misogyny and communal hatred in text data in multiple languages. additional challenge was introduced by code mixing and code switching. we use a cnn-lstm based neural network for our prediction task. the steps undertaken are presented here. ## conclusion our model performs moderately on the aggression labels. however, in gender-bias and communally charged labels, it significantly under-performs. out of the four datasets, the model performs the best on hindi dataset, but accuracy declines in meitei and multilingual datasets. in the future, we aim to re train the model using sample weighting to obtain better results. we also aim to train using larger models to obtain better results.