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One of the main motivations behind social network analysis is the quest for understanding opinion formation and diffusion. Previous models have limitations, as they typically assume opinion interaction mechanisms based on thresholds which are either fixed or evolve according to a random process that is external to the social agent. Indeed, our empirical analysis on large real-world datasets such as Twitter, Meme Tracker, and Yelp, uncovers previously unaccounted for dynamic phenomena at population-level, namely the existence of distinct opinion formation phases and social balancing. We also reveal that a phase transition from an erratic behavior to social balancing can be triggered by network topology and by the ratio of opinion sources. Consequently, in order to build a model that properly accounts for these phenomena, we propose a new (individual-level) opinion interaction model based on tolerance. As opposed to the existing opinion interaction models, the new tolerance model assumes that individual's inner willingness to accept new opinions evolves over time according to basic human traits. Finally, by employing discrete event simulation on diverse social network topologies, we validate our opinion interaction model and show that, although the network size and opinion source ratio are important, the phase transition to social balancing is mainly fostered by the democratic structure of the small-world topology.
Our main addition in this revision is the thorough explanation of how real data from Twitter, MemeTracker and Yelp is used to observe and extract representative phases of opinion dynamics. We have added a statistical analysis for all datasets which corroborates with our mathematically proposed interaction model.
Also, we found it necessary to model non-participant agents in our simulations and explain how this new setting affects the previous simulation results. By increasing the ratio of non-participants over a certain limit, the distinct phases become less obvious.
Finally, we have compared the tolerance based model with a null model, namely a random-interacting social network. As a result, we show that the conclusions drawn for our model are only reproducible if we use tolerance, and only this in turn is capable of reproducing the real-world phenomena observed in the empirical data.
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