Mitigating Influence of Disinformation Propagation Using Uncertainty-Based Opinion Interactions

Abstract

For decades, the spread of disinformation in online social networks (OSNs) has been a serious social issue. Disinformation via social media can easily mislead people's beliefs toward or against an event that may mislead their behaviors based on the misbeliefs. The game theory approaches have been proposed under dynamic settings to limit the adverse influences of disinformation. It is a challenge to expand the users' game strategies from the spreading decisions to the possible opinion updating choices. This work proposes a game-theoretic opinion framework that can formulate dynamic opinions by a belief model called Subjective Logic (SL) and provide opinion updates on five types of users' interactions on OSN platforms. The opinions are updated based on user choices and user types through the game interactions among legitimate users, attackers, and a defender in an OSN. Via the extensive simulation experiments, the effectiveness of the opinion models of five decision-makers (DMs) is analyzed in terms of users believing or disbelieving disinformation in an epidemic model with parameter optimization. Our results show that while homophily-based DMs (H-DMs) introduce the highest opinion polarization, uncertainty-based DMs (U-DMs) can effectively filter untrustworthy users propagating disinformation.

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

“…These studies highlight the diverse applications of machine learning in understanding social media contents. Aguilera et al [12] and Guo et al [13] contributed to the field by applying bot detection for credibility analysis on Twitter and mitigating the influence of disinformation propagation, respectively for different scenarios. These studies underscore the importance of credibility and integrity in online contenst.…”