Polupanova, Kateryna

This research project, conducted in one of Lithuania’s cities, explores the integration processes of foreign nationals in the urban environment. Its primary aim is to identify key obstacles to migrants’ social, cultural, and professional inclusion, and to propose practical recommendations for improving adaptation at the community, municipal, and organizational levels. Special emphasis is placed on the role of media manipulation and disinformation targeting migrants, which often reinforces stereotypes, fuels xenophobia, and undermines trust in institutions. The study focuses particularly on Ukrainian refugees as a vulnerable group exposed to politicized narratives and hybrid informational threats. Using both empirical data and theoretical frameworks, the article investigates how migrants perceive and respond to such manipulations. The findings aim to inform more effective integration strategies and contribute to policy development rooted in democratic values and information.

In the conditions of the modern information society, expat communities are becoming important centers for the formation of opinions and attitudes towards various socio - political events. The research object: the phenomenon of spontaneous self - organization of online community. The research purpose: to conduct a comparative ana lysis of homogeneous emigrant communities (primarily from Ukraine, Russia and Belarus) in various European Union countries, allowing to identify external factors influencing the formatio n and development of mechanisms for combating information threats. The results of this research will have a significant potential contribution to the development of social sciences, in particular sociology, communication studies and information security sc ience. By investigating the mechanisms of self - organization, the project will provide the scientific community with new data on how systems of spontaneous social control of information arise and operate without direct state or commercial support. The knowl edge gained will contribute to the development of new theoretical models of community self - organization and can be used to shape integration and information security policies at the national and pan - European levels. This, in turn, will help strengthen demo cratic institutions and increase the resilience of societies to external information threats, which is a priority for the European Union, Lithuania and Ukraine.

Objective: This article explores the possibility of the early diagnosis of catastrophic events based on weak signal detection and information field monitoring. Data and Methods: Publications in The New York Times and The Guardian are used as a source of data. To analyze the dynamics of the information field, the concept of weak signals is applied. Text mining methods (text preprocessing, frequency analysis, word clouds) are used. Statistical methods for analyzing distributions and testing hypotheses are used to determine anomalies in keyword frequencies. Results: The study makes it possible to confirm the assumption that the information field begins to change some time before the real onset of catastrophic events. The changes in the information field that preceded the two selected catastrophic events – the COVID-19 pandemic and Russia’s military invasion of Ukraine – are analyzed. Statistically significant changes in the information field in both cases were observed long before the start of these events. It was revealed that deviations in the frequency of keyword mentions more than 4σ are anomalous, and can signal impending disasters. The assumption that it is possible to track the state of the information field through open media resources is also confirmed. Conclusions: The results of the study confirm the possibility of using the analysis of the information field for the early diagnosis of global catastrophes. The creation of a system for monitoring the information field using the methods of machine linguistics can contribute to the timely preparation and minimization of threats to economic security.