Approximating Contribution in Misinformation Mitigation
Summary: Defines a Shapley-value-grounded metric for fairly allocating misinformation-mitigation credit among influencers, conditioned on the observed cascade. VG-App uses unbiased, outcome-consistent node-wise sampling to approximate contributions efficiently, with provable accuracy guarantees. (summarized by gpt-6-luna on Oct 22 2026)
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Authors
- 1. Jinghao Wang (University of Technology Sydney)
- 2. Renzhe Miao (Zhejiang Gongshang University)
- 3. Yanping Wu (University of Technology Sydney)
- 4. Xiaoyang Wang (University of New South Wales)
- 5. Ying Zhang (University of Technology Sydney)
BibTeX Citation
@inproceedings{wang_sigmod27,
title = {{Approximating Contribution in Misinformation Mitigation}},
author = {Wang, Jinghao and Miao, Renzhe and Wu, Yanping and Wang, Xiaoyang and Zhang, Ying},
series = {{SIGMOD} '27},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3837096},
url = {https://dl.acm.org/doi/10.1145/3837096},
year = {2027}
}
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