This paper investigates the analysis of social media data for the identification of active discussions, periods of increased user activity, and the thematic classification of posts in online news communities of a small city. Using data obtained from the Russian-language social network VKontakte, a dataset consisting of posts and their associated comments was collected for the period from 2019 to 2024. Based on the collected data, a set of discussion activity indicators was proposed and used to construct a discussion activity criterion through principal component analysis. To identify the thematic categories of events, a predefined list of event categories was provided to several large language models together with the texts of the posts. The experimental results demonstrated a high degree of inter-model agreement in category annotation, as well as a relatively high level of agreement with expert judgments. These findings highlight the considerable potential of large language models for monitoring user reactions to various local and global events in online communities.
Artem Rostunov and Maksim Stankevich. Identifying Active Event Discussions and Topics through Social Media Analysis // XXVIII International Conference on Data Analytics and Management in Data Intensive Domains DAMDID/RCDL 2026, Dedicated to the Memory of A.N. Gorban, Nizhny Novgorod, Russia, October 20–23, 2026.