Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts
This addresses the problem of identifying hostile content in low-resource languages like Hindi for social media moderation, but it is incremental as it applies existing ensemble methods to a specific dataset.
The paper tackled hostility detection in Hindi social media posts by developing an ensemble model combining mBERT with ANN and XGBoost, achieving a weighted F1-score of ~0.969 for binary classification and ~0.61 for multi-label classification, ranking third in a competition.
As the reach of the internet increases, pejorative terms started flooding over social media platforms. This leads to the necessity of identifying hostile content on social media platforms. Identification of hostile contents on low-resource languages like Hindi poses different challenges due to its diverse syntactic structure compared to English. In this paper, we develop a simple ensemble based model on pre-trained mBERT and popular classification algorithms like Artificial Neural Network (ANN) and XGBoost for hostility detection in Hindi posts. We formulated this problem as binary classification (hostile and non-hostile class) and multi-label multi-class classification problem (for more fine-grained hostile classes). We received third overall rank in the competition and weighted F1-scores of ~0.969 and ~0.61 on the binary and multi-label multi-class classification tasks respectively.