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

TL;DR

This paper presents a novel, robust stochastic optimization approach using Learning Automata to fairly allocate mitigation resources in social networks, effectively reducing misinformation exposure amidst dynamic and uncertain conditions.

Contribution

It introduces a non-stationary knapsack model and a Learning Automata-based algorithm for fair misinformation mitigation in social networks, adaptable to changing misinformation statistics.

Findings

Outperforms existing methods in fairness and effectiveness

Robust to social network misinformation dynamics

Ensures equitable mitigation among users

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.