Analysis of Social Response to Incidents: A Comparative Study of Arkhangelsk and Yekaterinburg

The Aim of this study is to develop and validate a methodology for analyzing social media data to enhance the reliability of emergency response systems by enabling rapid identification, assessment of scale, location, and forecasting of crisis events based on regional specifics of public activity. To achieve this goal, a Python‑based program was developed to automate the collection, preprocessing, and comprehensive analysis of data from the VKontakte social network. The program was tested using data from two cities with different demographic characteristics and socio‑economic conditions, i.e., Arkhangelsk and Yekaterinburg. Methods. The research employed methods of data collection from open VKontakte groups, followed by preprocessing and comprehensive analysis. The analysis included text analysis (sentiment analysis and word cloud generation), mathematical analysis (entropy and entropy derivative calculations to assess activity dynamics), and external factor analysis (the influence of meteorological conditions, holidays, and weekends). Results. The study revealed significant regional differences in social activity levels across various categories of emergency situations. Activity levels in Arkhangelsk were at least twice as high as those in Yekaterinburg, despite the smaller population of the city. The nature of activity also differed significantly: sharp spikes in activity were observed in Yekaterinburg, while activity in Arkhangelsk was more evenly distributed. Seasonality manifested in increased activity during periods of technical work or extreme weather conditions. In the “Fire” category, both cities demonstrated high and sustained activity; however, sharper spikes were noted in Yekaterinburg, particularly at the end of March and beginning of April, potentially indicating major incidents. In the “Water Outage” category, two significant peaks in activity were recorded in Arkhangelsk, in April and onJuly 31 and August 1, possibly pointing to widespread water supply issues. In Yekaterinburg, activity in this category was lower but more frequent, likely reflecting minor disruptions or informational updates. Conclusion. Social networks serve as a valuable source of data for analyzing public reactions to emergency situations. The identified regional characteristics of user behavior high light the need to create adaptive monitoring and forecasting systems that account for the specific features of each region. Using data from social networks enhances the reliability and efficiency of response systems by enabling rapid determination of incident scale, location, and consequences, as well as identifying seasonal and local threats. The findings confirm the necessity of implementing automated analytical tools capable of promptly assessing situations. Social networks can act as indicators of seasonal and local threats, allowing for proactive risk preparation. The observed differences in activity levels between regions underscore the importance of considering local conditions when developing strategies for communication and crisis management. Active use of social networks as a platform for civic participation demonstrates their potential to strengthen interaction between the public and authorities during crises.