An Information Retrieval Approach to Building Datasets for Hate Speech Detection

TL;DR

This paper introduces a novel IR-inspired methodology for creating more comprehensive and reliable hate speech datasets on Twitter, addressing challenges of rarity and subjectivity, and demonstrates its effectiveness through a new benchmark dataset.

Contribution

It applies IR techniques like pooling and active learning to efficiently select tweets for annotation, and introduces task decomposition and rationale collection to improve annotation quality and dataset coverage.

Findings

New hate speech dataset with broader coverage

Significant drop in model accuracy on the new dataset

Enhanced annotation quality through rationale collection

Abstract

Building a benchmark dataset for hate speech detection presents various challenges. Firstly, because hate speech is relatively rare, random sampling of tweets to annotate is very inefficient in finding hate speech. To address this, prior datasets often include only tweets matching known "hate words". However, restricting data to a pre-defined vocabulary may exclude portions of the real-world phenomenon we seek to model. A second challenge is that definitions of hate speech tend to be highly varying and subjective. Annotators having diverse prior notions of hate speech may not only disagree with one another but also struggle to conform to specified labeling guidelines. Our key insight is that the rarity and subjectivity of hate speech are akin to that of relevance in information retrieval (IR). This connection suggests that well-established methodologies for creating IR test collections can be usefully applied to create better benchmark datasets for hate speech. To intelligently and efficiently select which tweets to annotate, we apply standard IR techniques of {\em pooling} and {\em active learning}. To improve both consistency and value of annotations, we apply {\em task decomposition} and {\em annotator rationale} techniques. We share a new benchmark dataset for hate speech detection on Twitter that provides broader coverage of hate than prior datasets. We also show a dramatic drop in accuracy of existing detection models when tested on these broader forms of hate. Annotator rationales we collect not only justify labeling decisions but also enable future work opportunities for dual-supervision and/or explanation generation in modeling. Further details of our approach can be found in the supplementary materials.