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Semantic Shifts of Psychological Concepts in Scientific and Popular Media Discourse: A Distributional Semantics Analysis of Russian-Language Corpora

arXiv:2604.00017v1 Announce Type: new Abstract: This article examines semantic shifts in psychological concepts across scientific and popular media discourse using methods of distributional semantics applied to Russian-language corpora. Two corpora were compiled: a scientific corpus of approximately 300 research articles from the journals Psychology. Journal of the Higher School of Economics and Vestnik of Saint Petersburg University. Psychology (767,543 tokens) and a popular science corpus consisting of texts from the online psychology platforms Yasno and Chistye kogntsii (1,199,150 tokens). After preprocessing (OCR recognition, lemmatization, removal of stop words and non-informative characters), the corpora were analyzed through frequency analysis, clustering, and the identification of semantic associations. The results reveal significant differences in vocabulary and conceptual framing between the two discourse types: scientific texts emphasize methodological and clinical terminol

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Orlova Anastasia · · 1 min read · 3 views
#cs.CL

arXiv:2604.00017v1 Announce Type: new Abstract: This article examines semantic shifts in psychological concepts across scientific and popular media discourse using methods of distributional semantics applied to Russian-language corpora. Two corpora were compiled: a scientific corpus of approximately 300 research articles from the journals Psychology. Journal of the Higher School of Economics and Vestnik of Saint Petersburg University. Psychology (767,543 tokens) and a popular science corpus consisting of texts from the online psychology platforms Yasno and Chistye kogntsii (1,199,150 tokens). After preprocessing (OCR recognition, lemmatization, removal of stop words and non-informative characters), the corpora were analyzed through frequency analysis, clustering, and the identification of semantic associations. The results reveal significant differences in vocabulary and conceptual framing between the two discourse types: scientific texts emphasize methodological and clinical terminology, while popular science materials foreground everyday experience and therapeutic practice. A comparison of semantic associations for key concepts such as burnout and depression shows that scientific discourse links these terms to psychological resources, symptomatology, and diagnostic constructs, whereas popular science discourse frames them through personal narratives, emotions, and everyday situations. These findings demonstrate a clear shift from precise professional terminology toward more generalized and experiential meanings in popular media discourse and confirm the effectiveness of distributional semantics methods for identifying semantic transformations of psychological concepts across different communicative contexts.

Executive Summary

The article presents a robust distributional semantics analysis of semantic shifts in psychological concepts across Russian-language scientific and popular media corpora. Utilizing corpora from academic journals and online psychology platforms, the study identifies clear divergences in lexical usage: scientific discourse centers on methodological, clinical, and diagnostic terminology, whereas popular science discourse privileges experiential, narrative, and therapeutic frames. The analysis of key concepts like burnout and depression reveals a marked divergence in semantic associations—scientific texts anchor these terms in diagnostic and resource-oriented constructs, while popular media situates them within personal stories and everyday contexts. The methodology—leveraging frequency analysis, clustering, and semantic association identification—is methodologically sound and effectively applied. The findings contribute meaningfully to understanding discourse dynamics in psychological communication and validate the utility of distributional semantics in cross-context linguistic analysis.

Key Points

  • ▸ Significant semantic divergence between scientific and popular media discourse in psychological concepts
  • ▸ Scientific texts emphasize methodological/clinical terminology; popular media foreground experiential/narrative frames
  • ▸ Key concepts like burnout and depression show distinct semantic associations across discourse types

Merits

Methodological Rigor

The use of distributional semantics with appropriate corpora and preprocessing steps demonstrates a high level of analytical precision and relevance to contemporary linguistic research.

Demerits

Scope Limitation

The study is confined to Russian-language corpora, limiting generalizability to non-Russian linguistic or cultural contexts; broader cross-linguistic validation is needed.

Expert Commentary

This study is a commendable contribution to computational linguistics and applied psychology. The authors effectively bridge the gap between computational methods and psychological discourse analysis by applying distributional semantics to a real-world, culturally specific problem. Their identification of a clear semantic shift from clinical precision to experiential generalization is both empirically supported and theoretically significant. Importantly, the work aligns with broader trends in digital humanities and cognitive science that seek to quantify subtle shifts in meaning across communicative domains. One nuanced observation: the absence of longitudinal data precludes analysis of the trajectory of these shifts over time, which could be a valuable extension of future research. Moreover, the potential for replicating this analysis in other languages or domains—e.g., medical or legal—opens a new avenue for interdisciplinary inquiry. Overall, this work exemplifies how quantitative linguistic tools can illuminate complex human communication phenomena with measurable impact.

Recommendations

  • ✓ 1. Extend the analysis to cross-linguistic corpora to validate the universality of the identified semantic shifts.
  • ✓ 2. Incorporate qualitative interviews with authors or editors to contextualize the computational findings within human editorial decision-making.

Sources

Original: arXiv - cs.CL

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