Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917305483526144 |
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| author | Li, Yaqiong Zhang, Peng Hou, Peixu Tu, Kainan Zhang, Guangping Qu, Shan Chen, Wenshi Chen, Yan Gu, Ning Lu, Tun |
| author_facet | Li, Yaqiong Zhang, Peng Hou, Peixu Tu, Kainan Zhang, Guangping Qu, Shan Chen, Wenshi Chen, Yan Gu, Ning Lu, Tun |
| contents | Online power-asymmetric conflicts are prevalent, and most platforms rely on human moderators to conduct moderation currently. Previous studies have been continuously focusing on investigating human moderation biases in different scenarios, while moderation biases under power-asymmetric conflicts remain unexplored. Therefore, we aim to investigate the types of power-related biases human moderators exhibit in power-asymmetric conflict moderation (RQ1) and further explore the influence of AI's suggestions on these biases (RQ2). For this goal, we conducted a mixed design experiment with 50 participants by leveraging the real conflicts between consumers and merchants as a scenario. Results suggest several biases towards supporting the powerful party within these two moderation modes. AI assistance alleviates most biases of human moderation, but also amplifies a few. Based on these results, we propose several insights into future research on human moderation and human-AI collaborative moderation systems for power-asymmetric conflicts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01457 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts Li, Yaqiong Zhang, Peng Hou, Peixu Tu, Kainan Zhang, Guangping Qu, Shan Chen, Wenshi Chen, Yan Gu, Ning Lu, Tun Human-Computer Interaction Computation and Language Online power-asymmetric conflicts are prevalent, and most platforms rely on human moderators to conduct moderation currently. Previous studies have been continuously focusing on investigating human moderation biases in different scenarios, while moderation biases under power-asymmetric conflicts remain unexplored. Therefore, we aim to investigate the types of power-related biases human moderators exhibit in power-asymmetric conflict moderation (RQ1) and further explore the influence of AI's suggestions on these biases (RQ2). For this goal, we conducted a mixed design experiment with 50 participants by leveraging the real conflicts between consumers and merchants as a scenario. Results suggest several biases towards supporting the powerful party within these two moderation modes. AI assistance alleviates most biases of human moderation, but also amplifies a few. Based on these results, we propose several insights into future research on human moderation and human-AI collaborative moderation systems for power-asymmetric conflicts. |
| title | Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2603.01457 |