Modeling and Analysis of Uncertainty-Based False Information Propagation in Social Networks

To stop or mitigate the dissemination of false information in social networks, many studies have investigated the minimum number of seeding nodes required to significantly reduce the impact of false information. Although a person's confidence level, such as perceived certainty, and/or prior belief towards a given proposition can significantly affect their decision of whether to believe in true or false information, these topics have not been studied to date. In this work, we propose an opinion model based on Subjective Logic (SL), defining an opinion in belief, disbelief, and uncertainty, to study how to eradicate or mitigate the impact of false information by propagating true information to counter it. In the current form of an opinion in SL, when two agents interact with each other and update their opinions based on a consensus operator offered by SL, uncertainty continuously reduces whenever any new information, even conflicting evidence, is received. However, in reality, if a body of evidence is conflicting, with equal amounts supporting opposite positions, people often tend to be confused, leading to higher level of uncertainty. We enhance SL to deal with conflicting information associated with uncertainty. We map agents' opinion composition into each state in the SIR (Susceptible-Infected- Recovered) model to estimate the proportion of recovered agents who believe in true information. Our results show that agents' prior belief unfavoring false information can help guide their decisions towards a belief in true information even under high uncertainty. Further, having more true informers in a network can significantly increase the number of agents who believe in true information and the effect is more pronounced than having more frequent propagation of true information by fewer true informers.