Transfer Learning for Hate Speech Detection in Social Media

Abstract

Today, the internet is an integral part of our daily lives, enabling people to be more connected than ever before. However, this greater connectivity and access to information increase exposure to harmful content such as cyber-bullying and cyber-hatred. Models based on machine learning and natural language offer a way to make online platforms safer by identifying hate speech in web text autonomously. However, the main difficulty is annotating a sufficiently large number of examples to train these models. This paper uses a transfer learning technique to leverage two independent datasets jointly and builds a single representation of hate speech. We build an interpretable two-dimensional visualization tool of the constructed hate speech representation -- dubbed the Map of Hate -- in which multiple datasets can be projected and comparatively analyzed. The hateful content is annotated differently across the two datasets (racist and sexist in one dataset, hateful and offensive in another). However, the common representation successfully projects the harmless class of both datasets into the same space and can be used to uncover labeling errors (false positives). We also show that the joint representation boosts prediction performances when only a limited amount of supervision is available. These methods and insights hold the potential for safer social media and reduce the need to expose human moderators and annotators to distressing online messaging.

References in corpus (4)

• Frustratingly Easy Domain Adaptation
• Deep Learning for Hate Speech Detection in Tweets
• Detecting Offensive Language in Tweets Using Deep Learning
• Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations

Cited by in corpus (6)

• Directions in Abusive Language Training Data: Garbage In, Garbage Out
• Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model
• A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU
• Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures
• Contextual Hate Speech Detection in Code Mixed Text using Transformer Based Approaches
• Studying Socially Unacceptable Discourse Classification (SUD) through different eyes: "Are we on the same page ?"