Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization

Author:

Abouzeid Ahmed,Granmo Ole-Christoffer,Webersik Christian,Goodwin Morten

Abstract

Recent social networks' misinformation mitigation approaches tend to investigate how to reduce misinformation by considering a whole-network statistical scale. However, unbalanced misinformation exposures among individuals urge to study fair allocation of mitigation resources. Moreover, the network has random dynamics which change over time. Therefore, we introduce a stochastic and non-stationary knapsack problem, and we apply its resolution to mitigate misinformation in social network campaigns. We further propose a generic misinformation mitigation algorithm that is robust to different social networks' misinformation statistics, allowing a promising impact in real-world scenarios. A novel loss function ensures fair mitigation among users. We achieve fairness by intelligently allocating a mitigation incentivization budget to the knapsack, and optimizing the loss function. To this end, a team of Learning Automata (LA) drives the budget allocation. Each LA is associated with a user and learns to minimize its exposure to misinformation by performing a non-stationary and stochastic walk over its state space. Our results show how our LA-based method is robust and outperforms similar misinformation mitigation methods in how the mitigation is fairly influencing the network users.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards misinformation mitigation on social media: novel user activity representation for modeling societal acceptance;Journal of Computational Social Science;2024-03-22

2. REDRESS: Generating Compressed Models for Edge Inference Using Tsetlin Machines;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023-09-01

3. Gateway Entities in Problematic Trajectories;Proceedings of the ACM Web Conference 2023;2023-04-30

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