Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models

TL;DR

This paper explores how online community and author context influence the performance and biases of NLP deception detection models, revealing that author traits are stronger predictors of deception and highlighting the need for nuanced evaluation.

Contribution

It introduces an analysis of community and author context to explain model performance and biases in deception detection, emphasizing the importance of sub-population analysis.

Findings

Author characteristics better predict deceptive content than community traits.

Both author and community features significantly affect model accuracy.

Traditional metrics may overlook poor performance on specific sub-populations.

Abstract

Deceptive news posts shared in online communities can be detected with NLP models, and much recent research has focused on the development of such models. In this work, we use characteristics of online communities and authors -- the context of how and where content is posted -- to explain the performance of a neural network deception detection model and identify sub-populations who are disproportionately affected by model accuracy or failure. We examine who is posting the content, and where the content is posted to. We find that while author characteristics are better predictors of deceptive content than community characteristics, both characteristics are strongly correlated with model performance. Traditional performance metrics such as F1 score may fail to capture poor model performance on isolated sub-populations such as specific authors, and as such, more nuanced evaluation of deception detection models is critical.