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The key to obtaining a mechanistic and reliable understanding of complex public opinion formation processes is to identify the main mechanism governing interpersonal influence. Researchers have long been exploring simple yet predictive mathematical models of opinion dynamics. Although most models are based on the assumption that individuals update their opinions by averaging others' opinions, researchers might need to rethink this universally adopted micro-foundation. The deceivingly simple weighted-averaging mechanism features a non-negligible unrealistic implication, which brings unnecessary difficulties in seeking a proper balance between model complexity and predictive power. In this paper, we fundamentally resolve this problem by proposing the weighted-median mechanism as a new micro-foundation of opinion dynamics. Such an inconspicuous change from averaging to median leads to rich consequences. The weighted-median mechanism, derived from the cognitive dissonance theory in psychology, is well supported by online experiment data. It also broadens the applicability of opinion dynamics models to multiple-choice issues with ordered discrete options, e.g., political elections. Moreover, comparative studies show that the weighted-median mechanism predicts various real-world patterns of opinion evolution while some widely studied averaging-based models fail to, including how group structure affects the likelihood of reaching consensus and how extreme opinions are located in social networks. ; ISSN:2643-1564

Words matter in politics. The rhetoric that political elites employ structures civic discourse. The emergence of social media platforms as a medium of politics has enabled ordinary citizens to express their ideological inclinations by adopting the lexicon of political elites. This avails to researchers a rich new source of data in the study of political ideology. However, existing ideological text-scaling methods fail to produce meaningful inferences when applied to the short, informal style of textual content that is characteristic of social media platforms such as Twitter. This paper introduces the first viable approach to the estimation of individual-level ideological positions derived from social media content. This method allows us to position social media users—be they political elites, parties, or citizens—along a shared ideological dimension. We validate the proposed method by demonstrating correlation with existing measures of ideology across various political contexts and multiple languages. We further demonstrate the ability of ideological estimates to capture derivative signal by predicting out-of-sample, individual-level voting intentions. We posit that social media data can, when properly modeled, better capture derivative signal than discrete scales used in more traditional survey instruments.

International audience Opinion evolution and judgment revision are mediated through social influence. Based on a crowdsourced in vitro experiment, it is shown how a consensus model can be used to predict opinion evolution in online collective behaviour. It is the first time the predictive power of a quantitative model of opinion dynamics is tested against a real dataset. The model is parametrized by the influenceability of each individuals, a factor representing to what extent individuals incorporate external judgments. Judgment revision includes unpredictable variations which limit the potential for prediction. This level of unpredictability is measured via a specific control experiment. More than two thirds of the prediction errors are found to occur due to unpredictability of the human judgment revision process rather than to model imperfection.