A Guide to Misinformation Detection Datasets

• URL: http://arxiv.org/abs/2411.05060v1
• Date: Thu, 07 Nov 2024 18:47:39 GMT
• Title: A Guide to Misinformation Detection Datasets
• Authors: Camille Thibault, Gabrielle Peloquin-Skulski, Jacob-Junqi Tian, Florence Laflamme, Yuxiang Guan, Reihaneh Rabbany, Jean-François Godbout, Kellin Pelrine,
• Abstract summary: This guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations. All datasets and other artifacts are available at https://misinfo-datasets.complexdatalab.com/.
• Score: 5.673951146506489
• License:
• Abstract: Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this problem, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims. 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 insufficient label quality, spurious correlations, or political bias. 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. We discuss alternatives to mitigate this problem. Overall, this guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at https://misinfo-datasets.complexdatalab.com/.

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