Networks Seminar

Models of opinion dynamics describe how opinions are shaped in various environments. While these models are able to replicate general opinion distributions observed in real-world scenarios, their capacity to align with data at the user level remains mostly untested. In this talk, I will demonstrate that the MultiState Voter model with Zealots (MSVZ) can capture individual opinions in a fine-grained Twitter dataset collected during the 2017 French presidential elections. I will first lay out the theoretical background, with formulas to evaluate opinion fluctuation at the node level. I will also present exact equations to compute the probability of discord between nodes, that is, the probability that any two nodes hold different opinions. In particular, I will highlight how discord levels between two nodes hinge heavily on the statistical dependency between their states, which can be seen as a topological property of the underlying network. I will briefly illustrate how discord levels fluctuate in synthetic examples. I will then focus on the Twitter dataset, consisting of 19K accounts with pre-identified political leanings. Fitting the MSVZ on the retweet network with accounts of political parties as zealots, I will reveal a strong correspondence between individual opinion distributions in the equilibrium state and ground-truth political leanings. Additionally, I will demonstrate how discord probabilities can accurately identify pairs of like-minded users. These results depend heavily on taking into account the directionality and/or the weight of edges. Overall, this contribution advances our theoretical knowledge of the Voter Model, emphasizes its ability to identify individual characteristics in real-world datasets, and advocates for further empirical evaluations of opinion dynamics models at the user level.