Emigration narratives relevance assessment by large language models for social media monitoring

Section: Artificial Intelligence in Media and Communication Studies

The paper’s relevance lies in the lack of methodological experiments specifically focused on evaluating the heuristics of artificial intelligence (AI) for assessing users’ social sentiments expressed through digital markers, particularly in the context of Russian national audience’s emigration intentions. The article delves into studies that measure language and text as data, with a special focus on understanding users’ social attitudes and behaviors expressed through digital markers. The primary objective of the study is to assess the significance of the streams downloaded by the neural network, specifically the LSTM language model employed by the Medialogia service. The research design encompasses cognitive mapping (a preliminary stage to identify search queries) and social media analysis conducted by the Medialogia service. Additionally, a representative sample of the downloaded streams is manually analyzed to evaluate their relevance. The paper highlights common errors in creating search queries and provides strategies to overcome these inaccuracies, which can be further utilized to enhance the neural network’s ability to download relevant datasets. Furthermore, the relevance of the streams segmentation conducted by the language model is analyzed. The paper makes an assumption about the underlying reasons for the varying degrees of relevance of documents (posts) downloaded by the service.

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To cite this article: Dombrovskaya A. Yu., Brodovskaya E. V. (2025) Otsenka relevantnosti identifikatsii emigratsionnykh tekstov bol’shimi yazykovymi modelyami dlya monitoringa sotsial’nykh media [Emigration narratives relevance assessment by large language models for social media monitoring]. Vestnik Moskovskogo Universiteta. Seriya 10. Zhurnalistika 5: 209–232. DOI: 10.55959/msu.vestnik.journ.5.2025.209232