Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalisation of Misinformation Detection Models

• URL: http://arxiv.org/abs/2410.18122v1
• Date: Sat, 12 Oct 2024 09:46:36 GMT
• Title: Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalisation of Misinformation Detection Models
• Authors: Ivo Verhoeven, Pushkar Mishra, Ekaterina Shutova,
• Abstract summary: Misinfo-general is a benchmark dataset for evaluating misinformation models' ability to perform out-of-distribution generalisation. We identify 6 axes of generalisation-time, event, topic, publisher, political bias, misinformation type-and design evaluation procedures for each.
• Score: 15.120606566150816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces misinfo-general, a benchmark dataset for evaluating misinformation models' ability to perform out-of-distribution generalisation. Misinformation changes rapidly, much quicker than moderators can annotate at scale, resulting in a shift between the training and inference data distributions. As a result, misinformation models need to be able to perform out-of-distribution generalisation, an understudied problem in existing datasets. We identify 6 axes of generalisation-time, event, topic, publisher, political bias, misinformation type-and design evaluation procedures for each. We also analyse some baseline models, highlighting how these fail important desiderata.

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