Equal Truth: Rumor Detection with Invariant Group Fairness (2025)

Abstract: Due to the widespread dissemination of rumors on social media platforms, detecting rumors has been a long-standing concern for various communities. However, existing rumor detection methods rarely consider the fairness issues inherent in the model, which can lead to biased predictions across different stakeholder groups (e.g., domains and originating platforms of the detected content), also undermining their detection effectiveness. In this work, we propose a two-step framework to address this issue. First, we perform unsupervised partitioning to dynamically identify potential unfair data patterns without requiring sensitive attribute annotations. Then, we apply invariant learning to these partitions to extract fair and informative feature representations that enhance rumor detection. Extensive experiments show that our method outperforms strong baselines regarding detection and fairness performance, and also demonstrate robust performance on out-of-distribution samples. Further empirical results indicate that our learned features remain informative and fair across stakeholder groups and can correct errors when applied to existing baselines.

## introduction social media has reshaped the convenience of how people exchange their daily information, but it has also facilitated the spread of rumors via the internet. a rumor, defined as a piece of fabricated information, aims to mislead the public and generate illegal profits. therefore, detecting rumors accurately and promptly has become a shared goal in society. previously, most rumor detectors @xcite @xcite @xcite utilize data-driven methods to learn news content representations for rumor classification. this follows an ideal causal pathway @xmath0, where x denotes news content * corresponding author ## method problem formulation let @xmath1, y, s} represent a rumor detection instance, where x denotes the initial embedded feature, y denotes the classification label, and @xmath2, . . . , s n } denotes the sensitive attribute vectors indicating group membership of different sets (e.g., s 1 indicates the domain, s 2 indicates the platform). given a rumor detector with a base model φ(•) 1 for feature extraction and a classifier f (•), we aim to optimize both for accurate binary prediction @xmath3, non-rumor} while ensuring fairness across groups from all po-tential sets. s serves only for fairness evaluation. we omit the subscript when the context is clear. overview the overall framework of our method is shown in fig. 2 . our method starts with standard training, then iteratively performs two steps: (1) unfair partition, which discovers data partitions that degrade the performance of the base model, and (2) invariant learning, which aims to improve and balance performance in these partitions. ## conclusion in this work, we propose firm, which addresses the task of rumor detection from a multi-group fairness perspective, supported by extensive experiments that yield insightful findings. we specifically summarize that: (1) current rumor detection methods exhibit inferior detection effectiveness and fairness across different stakeholder groups, primarily due to confounding sensitive attributes present in real-world data;