Discovering and Categorising Language Biases in Reddit

TL;DR

This paper introduces a data-driven method using word embeddings to automatically discover and categorize language biases in Reddit communities, addressing limitations of previous approaches that rely on predefined bias sets.

Contribution

The study presents a novel approach that detects community-specific biases in Reddit data without predefined bias categories, suitable for smaller and slang-rich datasets.

Findings

Successfully identified gender, religion, and ethnic biases in Reddit communities.

Validated the method by comparing biases with those in Google News dataset.

Demonstrated the approach's effectiveness in community-centric online discourse.

Abstract

We present a data-driven approach using word embeddings to discover and categorise language biases on the discussion platform Reddit. As spaces for isolated user communities, platforms such as Reddit are increasingly connected to issues of racism, sexism and other forms of discrimination. Hence, there is a need to monitor the language of these groups. One of the most promising AI approaches to trace linguistic biases in large textual datasets involves word embeddings, which transform text into high-dimensional dense vectors and capture semantic relations between words. Yet, previous studies require predefined sets of potential biases to study, e.g., whether gender is more or less associated with particular types of jobs. This makes these approaches unfit to deal with smaller and community-centric datasets such as those on Reddit, which contain smaller vocabularies and slang, as well as biases that may be particular to that community. This paper proposes a data-driven approach to automatically discover language biases encoded in the vocabulary of online discourse communities on Reddit. In our approach, protected attributes are connected to evaluative words found in the data, which are then categorised through a semantic analysis system. We verify the effectiveness of our method by comparing the biases we discover in the Google News dataset with those found in previous literature. We then successfully discover gender bias, religion bias, and ethnic bias in different Reddit communities. We conclude by discussing potential application scenarios and limitations of this data-driven bias discovery method.