Dynamics of Uncertain and Conflicting Opinions in Social Networks

In this paper, we study the evolution of opinions where people are not sure of their own opinions and/or their opinions may be conflicting to others' in social networks. We model two types of agents so-called informed agents (IAs) and uninformed agents (UIAs). The IAs have a strong opinion agreeing or disagreeing toward a proposition without being influenced by other agents' opinions and have a high confidence (low uncertainty) toward its own opinion. The UIAs have a weak opinion without either agreeing or disagreeing toward a proposition and lack confidence with a high uncertainty. Based on subjective logic, we consider a binomial opinion to deal with an opinion with a degree of uncertainty. We develop two types of trust attitudes for agents to update their opinions upon their interactions with other agents: uncertainty-based trust (UT) and similarity-based trust (ST). In the UT, a UIA updates its opinion based on an interacting agent's uncertainty toward a proposition. In the ST, a UIA updates its opinion based on the degree of similarity between its own opinion and the interacting agent's opinion toward the proposition. Our results show that more IAs slow down the convergence of the opinions under the UT while they can quickly lead to opinion convergence under the ST. In addition, the ST leads uncertain opinions to two extremes, either 0 or 1, if consensus exists. On the other hand, the UT can make opinions converge to a certain point between two extreme opinions although the converged point is significantly affected by the dominant agents' opinions. Furthermore, we observe that under the UT, more IAs with a high centrality increase dissonance of opinions, while more IAs with a low centrality offer better chances for opinion consensus in both the UT and the ST.