The 2016 United States presidential election has been characterized as a period of extreme divisiveness that was exacerbated on social media by the influence of fake news, trolls, and social bots. However, the extent to which the public became more polarized in response to these influences over the course of the election is not well understood. In this paper we propose IdeoTrace, a framework for (i) jointly estimating the ideology of social media users and news websites and (ii) tracing changes in user ideology over time. We apply this framework to the last two months of the election period for a group of 47508 Twitter users and demonstrate that both liberal and conservative users became more polarized over time.
Opinion dynamics models typically either ignore influences on ideology external to the social network between users, or treat these influences as known inputs. The early, well-established work on this problem centered around the development of theoretical models that, given a social network of users with an initial distribution of opinions, use rule-based updates often inspired by physical or biological networks to estimate user opinion over multiple time steps. The models are then shown in simulation to converge to particular distributions based on the network and update rules. For example, in @cite_15 it was shown that modelling confirmation bias by moving users with similar opinions closer together each simulation step and breaking the network link between them otherwise, often results in the formation of a bimodal opinion distribution. For a review of popular opinion dynamics models, we refer to @cite_40 .
@cite_40 @cite_15
2089209982 1991442303 61072750 2013540006
We introduce a statistical-physics model for opinion dynamics on random networks where agents adopt the opinion held by the majority of their direct neighbors only if the fraction of these neighbors exceeds a certain threshold, p(u). We find a transition from total final consensus to a mixed phase where opinions coexist amongst the agents. The relevant parameters are the relative sizes in the initial opinion distribution within the population and the connectivity of the underlying network. As the order parameter we de. ne the asymptotic state of opinions. In the phase diagram we find regions of total consensus and a mixed phase. As the "laggard parameter" pu increases the regions of consensus shrink. In addition we introduce rewiring of the underlying network during the opinion formation process and discuss the resulting consequences in the phase diagram. Copyright (c) EPLA, 2008. Models of the convergence of opinion in social systems have been the subject of considerable recent attention in the physics literature. These models divide into two classes, those in which individuals form their beliefs based on the opinions of their neighbors in a social network of personal acquaintances, and those in which, conversely, network connections form between individuals of similar beliefs. While both of these processes can give rise to realistic levels of agreement between acquaintances, practical experience suggests that opinion formation in the real world is not a result of one process or the other, but a combination of the two. Here we present a simple model of this combination, with a single parameter controlling the balance of the two processes. We find that the model undergoes a continuous phase transition as this parameter is varied, from a regime in which opinions are arbitrarily diverse to one in which most individuals hold the same opinion. We study discrete opinion dynamics in a social network with "stubborn agents" who influence others but do not change their opinions. We generalize the classical voter model by introducing nodes (stubborn agents) that have a fixed state. We show that the presence of stubborn agents with opposing opinions precludes convergence to consensus; instead, opinions converge in distribution with disagreement and fluctuations. In addition to the first moment of this distribution typically studied in the literature, we study the behavior of the second moment in terms of network properties and the opinions and locations of stubborn agents. We also study the problem of "optimal placement of stubborn agents" where the location of a fixed number of stubborn agents is chosen to have the maximum impact on the long-run expected opinions of agents. This paper treats the opinion dynamics of an unequal, initial opinion distribution. We simulate the Deffuant model on a directed Barabasi–Albert network with discrete opinions and several subjects. We notice a focusing of the the resulting opinion distribution during the simulation towards the average value of the initial opinion distribution. A small change of the focusing is seen. A dependency of this change on the number of subjects and opinions is detected and indicates the change as a consequence of discretization of the opinions. Hereby the average value of the initial opinion distribution can be identified as the guide of opinion forming.