Our website uses cookies. By continuing to use this site, you agree to our use of cookies in accordance with this notice and the Terms of Use.
Journals KemSU
Scientific journal "Bulletin of KemSU. Series: Humanities and Social Sciences Scientific journal "Bulletin of KemSU. Series: Political, Sociological and Economic sciences Scientific journal "Bulletin of KemSU. Series: Biological, Engineering and Earth Sciences SibScript (until February 17, 2023 – Bulletin of KemSU Strategizing: Theory and Practice Foods and Raw Materials Virtual Communication and Social Networks Food Processing: Techniques and Technology Dairy industry Cheese- and buttermaking Science Evolution
Submit manuscript
Virtual Communication and Social Networks
VAK Russia 5.9.9 UDC 80 CSCSTI 16.31 Russian Classification of Professions by Education 45.06.01 Russian Library and Bibliographic Classification 80 Russian Trade and Bibliographic Classification 84 BISAC LAN000000 CSCSTI 16.21 Russian Library and Bibliographic Classification 81 Russian Trade and Bibliographic Classification 841

APPLYING SOCIAL NETWORK ANALYSIS TO RISK COMMUNICATION IN THE PROFESSIONAL SEGMENT OF NEWS MEDIA DISCOURSE Applying Social Network Analysis to Risk Communication in the Professional Segment of News Media Discourse

Published in Virtual Communication and Social Networks · Volume 4, Issue 2 · Pages 144–152 · Rubric: Media Communications and Journalism
DOI: https://doi.org/10.21603/2782-4799-2025-4-2-144-152 · EDN: JTMKKI
Received: 30.12.2024 Accepted: 19.02.2025 Published: 04.07.2025
Authors
EK
Ekaterina Pleshkova
ORCID Scopus ResearcherID Scholar SPIN
ABSTRACT
The authors applied the method of social network analysis to official and business medical discourse during the COVID-19 pandemic to examine the risk communication strategies. The corpus comprised news published on the official websites of the Russian Ministry of Health and the Federal Service for Consumer Protection and Welfare (Rospotrebnadzor) during the initial phase of restrictive measures in March 2020. The collected data underwent both mathematical processing and discourse analysis. The method of applied network analysis facilitated the visualization and interpretation of lexical representation of the pandemic in the professional news media discourse. The study utilized R Studio 4.4.1 and the Quanteda library with built-in base packages and the gsub function that replaces sections of lines. The topfeatures function revealed 30,723 most frequent lexical units. Findings indicate that medical news discourse predominantly adopted minimal to moderate communication strategies to mitigate public panic.
KEYWORDS
media discourse, risk communication, applied network analysis, news, natural language processing, lexical representation, COVID-19
Text References
Text
Text (PDF)
Read Download

1. Basarab M. A., Glinskaya E. V., Ivanov I. P., Kolesnikov A. V., Kuzovlev V. I. Study into the structure of the scientific coathorship graph using social network analysis. Voprosy kiberbezopasnosti, 2017, (1): 31–36. (In Russ.) https://elibrary.ru/xxhpvp

2. Burganova L. A., Iskhakova E. I. Risk-communication as an effective condition of risk management. The Review of Economy, the Law and Sociology, 2019, (1): 132–135. (In Russ.) https://elibrary.ru/gpfjof

3. Ivanov A. V. Trust and risk communication in the public space: A normative dimension. Nauka i sovremennost, 2015, (37-2): 65–70. (In Russ.) https://elibrary.ru/tufemj

4. Iskhakova E. I. Technologies of risk-communication in the field of international tourism: Media-waves in COVID-19 era. The Review of Economy, the Law and Sociology, 2020, (2): 144–147. (In Russ.) https://elibrary.ru/ekpfgf

5. Kozhemiakin E. A. Discourse analysis as an interdisciplinary project: Between a method and an ideology. Nauchnye vedomosti Belgorodskogo gosudarstvennogo universiteta. Seriia: Gumanitarnye nauki, 2015, (6): 5–12. (In Russ.) https://elibrary.ru/vwyzrf

6. Kupchinova T. V. Risk and risk communications. Filosofiia i sotsialnye nauki, 2009, (1-2): 48–52. (In Russ.)

7. Olomskaya N. N. On genre classification of media discourse. Nauchnyi Dialog, 2013, (5): 250–259. (In Russ.) https://elibrary.ru/qcqeix

8. Pleshkova E. K. Discoursive marking of the pandemia vocabulary: Network analysis of discourses. Relevant issues of linguistics and literary studies: Proc.  X (XXIV) Intern. Sci.-Prac. Conf., Tomsk, 13–15 Apr 2023. Tomsk: TSU, 2023, 97–103. (In Russ.) https://doi.org/10.17223/978-5-907572-02-7-2023-19

9. Pleshkova E. K., Rezanova Z. I. Effects of anti-epidemic (quarantine) measures on people during the COVID-19 pandemic: Applying social network analysis to identify the key topics. Bulletin of Siberian Medicine, 2024, 23(4): 120–128. (In Russ.) https://elibrary.ru/xucwom

10. Rezanova Z. I. Framing the pandemic situation in news discourse. Cognitive Studies of Language, 2024, (1-2): 327–330. (In Russ.) https://elibrary.ru/nycvnn

11. Rezanova Z. I., Stepanenko A. A. Discursive variants of thematic modeling of COVID-19 (news media discourse vs social networks). Tomsk State University Journal of Philology, 2023, (86): 84–101. (In Russ.) https://elibrary.ru/jrufis

12. Uvarova E. A. Mediatext and mediadiscourse: The problem of their correlation. Bulletin of the Moscow Region State University. Series: Linguistics, 2015, (5): 47–54. (In Russ.) https://elibrary.ru/vcqgfj

13. Shiriaeva O. V. Information and analytical media discourse as an object of communication and discourse research. Mediascope, 2012, (4). (In Russ.) https://elibrary.ru/pkzhjn

14. Ahmad W., Wang B., Martin P., Xu M., Xu H. Enhanced sentiment analysis regarding COVID-19 news from global channels. Journal of computational social science, 2023, 6(1): 19–57. https://doi.org/10.1007/s42001-022-00189-1

15. Bokaee Nezhad Z., Deihimi M. A. Twitter sentiment analysis from Iran about COVID 19 vaccine. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2022, 16(1). https://doi.org/10.1016/j.dsx.2021.102367

16. Singer N. Coronavirus media discourse and current situation (QCA for different responses to combat COVID-19). Humanities & Social Sciences Reviews, 2020, 8(3). https://elibrary.ru/sdbbfa

17. Stefanis C., Giorgi E., Kalentzis K., Tselemponis A., Nena E., Tsigalou C., Kontogiorgis C., Kourkoutas Y., Chatzak E., Dokas I., Constantinidis T., Bezirtzoglou E. Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models. Frontiers in Public Health, 2023, 11. https://doi.org/10.3389/fpubh.2023.1191730

18. Wang J., Fan Y., Palacios J., Chai Yu., Guetta-Jeanrenaud N., Obradovich N., Zhou Ch., Zheng S. Global evidence of expressed sentiment alterations during the COVID-19 pandemic. Nature Human Behaviour, 2022, 6(3): 349–358. https://elibrary.ru/xiqnug

19. Yakunin K., Mukhamediev R. I., Zaitseva E., Levashenko V., Yelis M., Symagulov A., Kuchin Ya., Muhamedijeva E., Aubakirov M., Gopejenko V. Mass media as a mirror of the COVID-19 pandemic. Computation, 2021, 9(12). https://doi.org/10.3390/computation9120140

This work is licensed under Creative Commons Attribution 4.0 International
PDF JATS XML
The contact persons
Nikolay D. Golev
Editor-in-Chief
Nikolay D. Golev
ngolevd@mail.ru
Lidiya G. Kim
Vice Editor-in-Chief
Lidiya G. Kim
kimli09@mail.ru
2993
downloads
VAK RINC Science Index Directory of Open Access Journals Crossref RINC Dimensions Scilit The Lens
CC
This work is licensed under Creative Commons Attribution 4.0 International Creative Commons
© КемГУ, 1997–2025
Agreement Personal data protection and processing policy Support Powered by Editorum, 2026 Instructions
Login or Create
* Forgot your password?