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As the reach of the internet increases, pejorative terms started floodingover social media platforms. This leads to the necessity of identifying hostilecontent on social media platforms. Identification of hostile contents onlow-resource languages like Hindi poses different challenges due to its diversesyntactic structure compared to English. In this paper, we develop a simpleensemble based model on pre-trained mBERT and popular classification algorithmslike Artificial Neural Network (ANN) and XGBoost for hostility detection inHindi posts. We formulated this problem as binary classification (hostile andnon-hostile class) and multi-label multi-class classification problem (for morefine-grained hostile classes). We received third overall rank in thecompetition and weighted F1-scores of ~0.969 and ~0.61 on the binary andmulti-label multi-class classification tasks respectively.

Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts