Статья боковой панель

Опубликована
31 Март 2023
Файлы
PDF
Статистика
Прочитано : 123 Число скачиваний : 86

Основная статья Содержание

Аннотация

Представлен аналитический обзор литературы, посвященный рассмотрению разнообразных эффектов и опосредующих переменных, связанных с воздействием информации в социальных сетях на мнения, эмоции и поведение людей в условиях пандемии COVID-19. В ходе выполнения работы: (1) показана роль анализа данных социальных сетей для измерения общественного мнения в отношении пандемии; (2) установлены эффекты воздействия социальных сетей на мнения, эмоции и поведение людей; (3) перечислены факторы, влияющие на распространение информации о COVID-19 в социальных сетях и эффективность ее воздействия; (4) обсуждены особенности борьбы с воздействием недостоверной информации о пандемии в социальных сетях. Помимо прочего, показано, что механизмы, лежащие в основе влияния социальных сетей на изменения в поведении людей по отношению к здоровью, заключаются в том, что освещение пандемии в социальных сетях может усиливать опасения общественности и побудить ее принять превентивные меры. Полученные результаты свидетельствуют о важности мониторинга актуальной ситуации и своевременного предоставления населению через СМИ (в том числе и социальные сети) точной и достоверной информации для повышения эмоционального благополучия и соблюдения профилактического поведения.

Ключевые слова

COVID-19 коронавирус пандемия социальные сети инфодемия дезинформация стигматизация мнения эмоции поведение анализ данных

Детали статьи

Об авторе

Александр Владимирович Ванин

кандидат психологических наук, научный сотрудник, лаборатория психологии и психофизиологии творчества Института психологии РАН, Москва, Россия

Как цитировать
[1]
Ванин, А.В. 2023. ВОЗДЕЙСТВИЕ РАСПРОСТРАНЯЕМОЙ В СОЦИАЛЬНЫХ СЕТЯХ ИНФОРМА-ЦИИ О ПАНДЕМИИ COVID-19 НА МНЕНИЯ, ЭМОЦИИ И ПОВЕДЕНИЕ. Учёные записки Института психологии РАН. 3, 1(7) (мар. 2023), 53–65.
Скачать ссылку
Endnote/Zotero/Mendeley (RIS)
BibTeX

Литература

  1. Kim, L., Fast, S.M., Markuzon, N. (2019). Incorporating media data into a model of infectious disease transmission. PLOS ONE, 14(2).
  2. Schild, L., Ling, C., Blackburn, J., Stringhini, G., Zhang, Y., Zannettou, S. (2020). «Go eat a bat, Chang!»: An Early Look on the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19. ArXiv, abs/2004.04046.
  3. Shi, W., Liu, D., Yang, J., Zhang, J., Wen, S., Su, J. (2020). Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter. International Journal of Environmental Research and Public Health, 17(22), 8701.
  4. Shahi, G.K., Dirkson, A., Majchrzak, T.A. (2021). An exploratory study of COVID-19 misinformation on Twitter. Online Social Networks and Media, 22, 100104.
  5. Chen, Q., Min, C., Zhang, W., Wang, G., Ma, X., Evans, R.D. (2020a). Unpacking the black box: How to promote citizen engagement through government social media during the COVID-19 crisis. Computers in Human Behavior, 110, 106380–106380.
  6. Kayes, A., Islam, S., Watters, P. A., Ng, A., Kayesh, H. (2020). Automated measurement of attitudes towards social distancing using social media: A COVID-19 case study. First Monday, 25(11).
  7. Saleh, S.N., Lehmann, C.U., McDonald, S.A., Basit, M.A., Medford, R.J. (2021). Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter. Infection Control and Hospital Epidemiology, 42(2), 131–138.
  8. Dashtian, H., Murthy, D. (2021). CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics, Sentiment and Location Information. Academia Letters.
  9. Agarwal, A., Salehundam, P., Padhee, S., Romine, W.L., Banerjee, T. (2020). Leveraging Natural Language Processing to Mine Issues on Twitter During the COVID-19 Pandemic. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 886–891).
  10. Medford, R.J., Saleh, S.N., Sumarsono, A., Perl, T.M., Lehmann, C.U. (2020). An "Infodemic": Leveraging High-Volume Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak. Med Rxiv.
  11. Gruchoła, M., Sławek-Czochra, M. (2021). “The culture of fear” of inhabitants of EU countries in their reaction to the COVID-19 pandemic-A study based on the reports of the Eurobarometer. Safety Science, 135, 105140.
  12. Oh, S.H., Lee, S.Y., Han, C. (2020). The Effects of Social Media Use on Preventive Behaviors during Infectious Disease Outbreaks: The Mediating Role of Self-relevant Emotions and Public Risk Perception. Health Communication, 1–10.
  13. Liao, H.-P., Wang, J.-L. (2021). The impact of epidemic information on the public’s worries and attitude toward epidemic prevention measures during the COVID-19 outbreak. Current Psychology, 1–9.
  14. Lin, Y., Hu, Z., Alias, H., Wong, L. P. (2020). Influence of Mass and Social Media on Psychobehavioral Responses Among Medical Students During the Downward Trend of COVID-19 in Fujian, China: Cross-Sectional Study. Journal of Medical Internet Research, 22(7).
  15. Wang, H., Li, Y., Hutch, M., Naidech, A., Luo, Y. (2021). Using Tweets to Understand How COVID-19-Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study. Journal of Medical Internet Research, 23(2).
  16. Ugarte, D.A., Cumberland, W.G., Flores, L., Young, S.D. (2021). Public Attitudes About COVID-19 in Response to President Trump’s Social Media Posts. JAMA Network Open, 4(2).
  17. Budhwani, H., Sun, R. (2020). Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the “Chinese virus” on Twitter: Quantitative Analysis of Social Media Data. Journal of Medical Internet Research, 22(5).
  18. Li, Y., Twersky, S., Ignace, K., Zhao, M., Purandare, R., Bennett-Jones, B., Weaver, S. R. (2020b). Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak. International Journal of Environmental Research and Public Health, 17(18), 6847.
  19. Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., … Dai, J. (2020). Mental health problems and social media exposure during COVID-19 outbreak. PLOS ONE, 15(4), 1–10.
  20. Ni, M.Y., Yang, L., Leung, C.M., Li, N., Yao, X.I., Wang, Y., Leung, G., Cowling, B.J., Liao, Q. (2020). Mental Health, Risk Factors, and Social Media Use During the COVID-19 Epidemic and Cordon Sanitaire Among the Community and Health Professionals in Wuhan, China: Cross-Sectional Survey. JMIR Mental Health, 7(5).
  21. Ahmad, A.R., Murad, H.R. (2020). The Impact of Social Media on Panic During the COVID-19 Pandemic in Iraqi Kurdistan: Online Questionnaire Study. Journal of Medical Internet Research, 22(5).
  22. Zhong, B., Jiang, Z., Xie, W., Qin, X. (2020). Association of Social Media Use With Mental Health Conditions of Nonpatients During the COVID-19 Outbreak: Insights from a National Survey Study. Journal of Medical Internet Research, 22(12).
  23. Zhu, Y., Cao, L., Xie, J., Yu, Y., Chen, A., Huang, F. (2021). Using social media data to assess the impact of COVID-19 on mental health in China. Psychological Medicine, 1–8.
  24. Király, O., Potenza, M.N., Stein, D.J., King, D.L., Hodgins, D.C., Saunders, J.B., Brand, M. (2020). Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance. Comprehensive Psychiatry, 100, 152180.
  25. Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C.M., Brugnoli, E., Schmidt, A.L., Zola, P., Zollo, F., Scala, A. (2020). The COVID-19 social media infodemic. Scientific Reports, 10 (1).
  26. Kamiński, M., Szymańska, C., Nowak, J.K. (2021). Whose Tweets on COVID-19 Gain the Most Attention: Celebrities, Political, or Scientific Authorities? Cyberpsychology, Behavior, and Social Networking, 24(2), 123–128.
  27. Allington, D., Duffy, B., Wessely, S., Dhavan, N., Rubin, J. (2020). Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency. Psychological Medicine, 1–7.
  28. Georgiou, N., Delfabbro, P., Balzan, R. (2020). COVID-19-related conspiracy beliefs and their relationship with perceived stress and pre-existing conspiracy beliefs. Personality and Individual Differences, 166(166), 110201.
  29. Pulido, C.M., Villarejo-Carballido, B., Redondo-Sama, G., Gómez, A. (2020). COVID-19 infodemic: More retweets for science-based information on coronavirus than for false information. International Sociology, 35(4).
  30. Liu, P.L. (2021). COVID-19 information on social media and preventive behaviors: Managing the pandemic through personal responsibility. Social Science & Medicine, 277, 113928–113928.
  31. Bridgman, A., Merkley, E., Loewen, P.J., Owen, T., Ruths, D., Teichmann, L., Zhilin, O. (2020). The causes and consequences of COVID-19 misperceptions: Understanding the role of news and social media. Harvard Kennedy School Misinformation Review, 1(3).
  32. Bish, A., Michie, S. (2010). Demographic and attitudinal determinants of protective behaviours during a pandemic: A review. British Journal of Health Psychology, 15(4), 797–824.
  33. Liao, H.-P., Wang, J.-L. (2021). The impact of epidemic information on the public’s worries and attitude toward epidemic prevention measures during the COVID-19 outbreak. Current Psychology, 1–9.
  34. Badur, S., Ota, M. O. C., Öztürk, S., Adegbola, R., Dutta, A. (2020). Vaccine confidence: the keys to restoring trust. Human Vaccines & Immunotherapeutics, 16(5), 1007–1017.
  35. Lyu, H., Wang, J., Wu, W., Duong, V., Zhang, X., Dye, T.D., Luo, J. (2020). Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination. ArXiv Preprint ArXiv:2012.02165.
  36. Mitra, T., Counts, S., Pennebaker, J.W. (2016). Understanding Anti-Vaccination Attitudes in Social Media. In 10th International Conference on Web and Social Media, ICWSM 2016 (pp. 269–278).
  37. Kim, H., Han, J. Y., Seo, Y. (2020). Effects of Facebook Comments on Attitude Toward Vaccines: The Roles of Perceived Distributions of Public Opinion and Perceived Vaccine Efficacy. Journal of Health Communication, 25(2), 159–169.
  38. Nuzhath, T., Tasnim, S., Sanjwal, R.K., Trisha, N.F., Rahman, M., Mahmud, S.M., Arman, A., Chakraborty, S., Hossain, M.M. (2020). COVID-19 vaccination hesitancy, misinformation and conspiracy theories on social media: A content analysis of Twitter data.
  39. Tangcharoensathien, V., Calleja, N., Nguyen, T., Purnat, T., D’Agostino, M., Garcia-Saiso, S., AbdAllah, A. (2020). Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation. Journal of Medical Internet Research, 22(6).
  40. Latynov, V.V., Vanin, A.V. (2021). Psychological Antecedents and Opportunities for Correcting Negative Attitudes towards COVID-19 Prevention Measures. IFAC-PapersOnLine, 54, 161–165.
  41. Walter, N., Brooks, J.J., Saucier, C.J., Suresh, S. (2020). Evaluating the Impact of Attempts to Correct Health Misinformation on Social Media: A Meta-Analysis. Health Communication, 1–9.
  42. Bode, L., Vraga, E.K. (2018). See Something, Say Something: Correction of Global Health Misinformation on Social Media. Health Communication, 33(9), 1131–1140.
  43. Kreps, S., Kriner, D.L. (2020). The Covid-19 Infodemic and the Efficacy of Corrections. SSRN Electronic Journal.
  44. Hopkins, D.J., Sides, J.M., Citrin, J. (2019). The Muted Consequences of Correct Information about Immigration. The Journal of Politics, 81, 315–320.
  45. Ecker, U.K., Hogan, J.L., Lewandowsky, S. (2017). Reminders and Repetition of Misinformation: Helping or Hindering Its Retraction? Journal of applied research in memory and cognition, 6 (2), 185–192.