Yesterday’s News: Benchmarking Multi-Dimensional Out-of-Distribution Generalization of Misinformation Detection Models
Year: 2025
Type: article
Abstract: This article introduces misinfo-general, a benchmark dataset for evaluating misinformation models’ ability to perform out-of-distribution generalization. Misinformation changes rapidly, much more quickly than moderators can annotate at scale, resulting in a shift between the training and inference data distributions. As a result, misinformation detectors need to be able to perform out-of-distribution generalization, an attribute they currently lack. Our benchmark uses distant labeling to enable simulating covariate shifts in misinformation content. We identify time, event, topic, publisher, political bias, and misinformation type as important axes for generalization, and we evaluate a common class of baseline models on each. Using article metadata, we show how this model fails desiderata, which is not necessarily obvious from classification metrics. Finally, we analyze properties of the data to ensure limited presence of modelling shortcuts. We make the dataset and accompanying code publicly available.1
Source: Computational Linguistics
DOI: 10.1162/coli.a.585
Institutions: University of Amsterdam, Meta (United Kingdom)
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404306290