Two Birds with One Stone: Improving Rumor Detection by Addressing the Unfairness Issue
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912172328615936 |
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| author | Chen, Junyi Wu, Mengjia Liu, Qian Ding, Ying Zhang, Yi |
| author_facet | Chen, Junyi Wu, Mengjia Liu, Qian Ding, Ying Zhang, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20671 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Two Birds with One Stone: Improving Rumor Detection by Addressing the Unfairness Issue Chen, Junyi Wu, Mengjia Liu, Qian Ding, Ying Zhang, Yi Social and Information Networks Machine Learning 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. |
| title | Two Birds with One Stone: Improving Rumor Detection by Addressing the Unfairness Issue |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2412.20671 |