A Guide to Misinformation Detection Data and Evaluation

TL;DR

This paper compiles and evaluates the largest collection of misinformation datasets, highlighting quality issues and proposing evaluation standards to improve research accuracy in misinformation detection.

Contribution

It introduces the largest curated misinformation datasets, assesses their quality, and proposes Evaluation Quality Assurance (EQA) to enhance evaluation practices.

Findings

Many datasets contain flaws like spurious correlations and ambiguity.

Categorical labels may not accurately reflect detection model performance.

Most datasets in the literature have significant quality issues.

Abstract

Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of 36 datasets that consist of statements or claims, as well as the 9 datasets that consist of data in purely paragraph form. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as spurious correlations, or examples that are ambiguous or otherwise impossible to assess for veracity. We find the latter issue is particularly severe and affects most datasets in the literature. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. Finally, we propose and highlight Evaluation Quality Assurance (EQA) as a tool to guide the field toward systemic solutions rather than inadvertently propagating issues in evaluation. Overall, this guide aims to provide a roadmap for higher quality data and better grounded evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at https://misinfo-datasets.complexdatalab.com/.