Two Birds with One Stone: Improving Rumor Detection by Addressing the Unfairness Issue
This work addresses fairness issues in rumor detection, which is important for social media platforms and users, but appears incremental as it builds on existing detectors.
The paper tackles the problem of degraded performance and group unfairness in rumor detection caused by confounding sensitive attributes, proposing a two-step framework that identifies these attributes and learns invariant representations to improve both detection accuracy and fairness.
The degraded performance and group unfairness caused by confounding sensitive attributes in rumor detection remains relatively unexplored. To address this, we propose a two-step framework. Initially, it identifies confounding sensitive attributes that limit rumor detection performance and cause unfairness across groups. Subsequently, we aim to learn equally informative representations through invariant learning. Our method considers diverse sets of groups without sensitive attribute annotations. Experiments show our method easily integrates with existing rumor detectors, significantly improving both their detection performance and fairness.