HateMonitors: Language Agnostic Abuse Detection in Social Media

September 27, 2019 · Entered Twilight · 🏛 Fire

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Authors Punyajoy Saha, Binny Mathew, Pawan Goyal, Animesh Mukherjee arXiv ID 1909.12642 Category cs.SI: Social & Info Networks Cross-listed cs.CL Citations 31 Venue Fire Repository https://github.com/punyajoy/HateMonitors-HASOC ⭐ 8 Last Checked 2 months ago

Abstract

Reducing hateful and offensive content in online social media pose a dual problem for the moderators. On the one hand, rigid censorship on social media cannot be imposed. On the other, the free flow of such content cannot be allowed. Hence, we require efficient abusive language detection system to detect such harmful content in social media. In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019. We have used a Gradient Boosting model, along with BERT and LASER embeddings, to make the system language agnostic. Our model came at First position for the German sub-task A. We have also made our model public at https://github.com/punyajoy/HateMonitors-HASOC .