Uncertainty-based False Information Propagation in Social Networks

Abstract

Many network scientists have investigated the problem of mitigating or removing false information propagated in social networks. False information falls into two broad categories: disinformation and misinformation. Disinformation represents false information that is knowingly shared and distributed with malicious intent. Misinformation in contrast is false information shared unwittingly, without any malicious intent. Many existing methods to mitigate or remove false information in networks concentrate on methods to find a set of seeding nodes (or agents) based on their network characteristics (e.g., centrality features) to treat. The aim of these methods is to disseminate correct information in the most efficient way. However, little work has focused on the role of uncertainty as a factor in the formulation of agents' opinions. Uncertainty-aware agents can form different opinions and eventual beliefs about true or false information resulting in different patterns of information diffusion in networks. In this work, we leverage an opinion model, called Subjective Logic (SL), which explicitly deals with a level of uncertainty in an opinion where the opinion is defined as a combination of belief, disbelief, and uncertainty, and the level of uncertainty is easily interpreted as a person's confidence in the given belief or disbelief. However, SL considers the dimension of uncertainty only derived from a lack of information (i.e., ignorance), not from other causes, such as conflicting evidence. In the era of Big Data, where we are flooded with information, conflicting information can increase uncertainty (or ambiguity) and have a greater effect on opinions than a lack of information (or ignorance). To enhance the capability of SL to deal with ambiguity as well as ignorance, we propose an SL-based opinion model that includes a level of uncertainty derived from both causes. By developing a variant of the Susceptible-Infected-Recovered epidemic model that can change an agent's status based on the state of their opinions, we capture the evolution of agents' opinions over time. We present an analysis and discussion of critical changes in network outcomes under varying values of key design parameters, including the frequency ratio of true or false information propagation, centrality metrics used for selecting seeding false informers and true informers, an opinion decay factor, the degree of agents' prior belief, and the percentage of true informers. We validated our proposed opinion model using both the synthetic network environments and realistic network environments considering a real network topology,

A Survey on Centrality Metrics and Their Network Resilience Analysis

“…They found that when the initial set of seed propagators are high-degree nodes, then the choice of neighboring nodes to spread the information does not affect the long-term propagation significantly. Network structure features, such as network topology, node in-degree, out-degree, edge weight, and clustering coefficient, have also been considered in studies of false information propagation [185], [186], [187], [188]. Cho et al [185] built an uncertainty-based subjective opinion model using a belief model, called Subjective Logic.…”

“…Network structure features, such as network topology, node in-degree, out-degree, edge weight, and clustering coefficient, have also been considered in studies of false information propagation [185], [186], [187], [188]. Cho et al [185] built an uncertainty-based subjective opinion model using a belief model, called Subjective Logic. They developed different types of agents that can propagate false information intentionally (i.e., disinformers) and mistakenly (i.e., misinformers), where true information is also propagated to counter the false information.…”

Online Social Deception and Its Countermeasures: A Survey

“…Cho et al [26] extended the basic SIR model by replacing the transition between states to a decision based on the agent's belief on the extent of uncertainty in the agent's opin-This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

“…In addition, in the SIR model, the state change is controlled by probability; but this autonomous behavior ignores a user's intention and belief. To complement this, there have been some efforts [26,96] focusing on modeling and evaluating the effect of subjective, uncertain opinion and trust of agents and the role of more agents in terms of false information diffusion.…”

“…where I(t) is the number of users (agent I) that spread the rumor tweet at time t. Tweets(t) is the number of tweets at time t from the real data. • Mean Fraction of Recovered Agents Per Time Unit (R) [26]: This is a specific case of the statistics and plot metric. Instead of plotting the count of each agent at each time point, the average fraction of recovered agents during the total session time T is calculated.…”