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Размежевание или сплочение? Динамика сетевой структуры политических телеграм-каналов: моделирование и эмпирический анализ
In this paper, the authors propose a new methodology to analyze the dynamics of large-scale online network structures caused by significant exogenous shocks (foreign policy crises and the onset of military conflicts). The focus is on diagnosing cohesion or polarization processes. On the first step, mechanisms at the micro-level that may lead to consolidation or polarization are analyzed based on existing theoretical traditions. Among these, mechanisms based on changes in position and those based on changes in identity are highlighted. On the second step, the «network projection» of these mechanisms' actions is determined: the changes in the structures of interaction networks between individuals they lead to. Here, mathematical modeling plays a key role, allowing for specific predictions regarding network behavior, expressed in changes in observable metrics. Four network indicators are proposed: the number of communities (clusters), modularity, the number of connections, and the average degree of nodes. As a result of computational experiments, the expected dynamics of the indicators, correlated with the corresponding mechanisms of changes in political positions or identity, are obtained. On the third step, these predictions are compared with observed empirical data. The analyzed dataset consists of 5.7 million Telegram channels messages, corresponding to the start of the special military operation (February - March 2022) and chronologically offset by ten months (April 2021 and December 2022). On this basis, a network based on hyperlinks between channels is constructed. For each time period, the same network parameters as for the computational data are measured. Comparing the observed dynamics with those predicted by the models indicates one primary mechanism – cohesion through the expansion of in-group boundaries, based on shifting to higher-order identity categories.