The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets

TL;DR

This systematic review analyzes hate speech datasets to reveal biases in target identity representation, highlighting gaps and positive trends in diversifying hate detection data for machine learning.

Contribution

It provides a comprehensive assessment of dataset biases and diversity in hate speech research, emphasizing the importance of inclusive data for fair ML-based moderation.

Findings

Skewed representation of target identities in datasets

Mismatch between conceptualized and included targets

Positive trend towards diversification of datasets

Abstract

Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evidence that they underperform in detecting hateful communications directed towards specific identities and may discriminate against them, we know surprisingly little about the provenance of such bias. To fill this gap, we present a systematic review of the datasets for the automated detection of hateful communication introduced over the past decade, and unpack the quality of the datasets in terms of the identities that they embody: those of the targets of hateful communication that the data curators focused on, as well as those unintentionally included in the datasets. We find, overall, a skewed representation of selected target identities and mismatches between the targets that research conceptualizes and ultimately includes in datasets. Yet, by contextualizing these findings in the language and location of origin of the datasets, we highlight a positive trend towards the broadening and diversification of this research space.