A Comparative Analysis of the Transformation of the Other’s Image: 2018-2022
pdf (Русский)
html (Русский)


The Other Models Internet Internet Queries Search Engines Food Сlothes Sexuality the Digital Other

How to Cite

Aliev, R., & Yakushenkova, O. (2022). A Comparative Analysis of the Transformation of the Other’s Image: 2018-2022. Galactica Media: Journal of Media Studies, 4(4), 47-62. https://doi.org/10.46539/gmd.v4i4.347


The article is the final in a series of studies we conducted in the period from 2018 to 2022, and focuses on the transformation of the image of the Other in the period of the Covid-19 pandemic. For this study, we used Internet query statistics, extracting a series of markers, which we divided into three groups: food, clothing(appearance), and sexuality. The data was used to compile a correlation matrix and identify the strongest correlation between the markers. The study showed that the most diverse in the number of different markers is the food aspect. The appearance and sexual aspects are less distinctive during the pandemic but also play an important role in shaping the Other's image. It is also worth mentioning the fact that in the post-Covid time (2022) the difference between various models is blurred and some of them are enlarged by the inclusion of representatives of other ethnic groups. In particular, today we can distinguish several big clusters of the Other’s models holding common structural markers: some models are united according to their “food” aspect (Far Eastern cluster), others according to their appearance and sexual aspects (cluster of the former Soviet Union ethnic groups). However, within these clusters, models also share structural markers, so that they can be combined into subgroups based on one feature or another.

pdf (Русский)
html (Русский)


Akhmadeev, D. V., & Yurevich, M. A. (2021). Predicting the unemployment rate: Analyzing statistics on search engine query. Terra Economicus, 19(3), 53–64. https://doi.org/10.18522/2073-6606-2021-19-3-53-64 (In Russian).

Aliev, R. T., & Yakushenkova, O. S. (2018). Alimentary Models of the Imaginary Alien. Caspian Region: Politics, Economics, Culture, 4, 160–166. (In Russian).

Aliev, R. T., & Yakushenkova, O. S. (2021a). Alimentary Models of the Ethnic Other in the (Post-)Covid Period. Journal of Frontier Studies, 6(3), 213–226. https://doi.org/10.46539/jfs.v6i3.322 (In Russian).

Aliev, R. T., & Yakushenkova, O. S. (2021b). The Image of the Other in the (post-)Covid Period: Analysis of Russian-language Internet Queries. Galactica Media: Journal of Media Studies, 3(4), 163–190. https://doi.org/10.46539/gmd.v3i4.237 (In Russian).

Aliev, R. T., & Yakushenkova, O. S. (2022). A Comparativistic Analysis of the Transformation of Alimentary Aspect of the Other’s Image: 2018-2022. Journal of Frontier Studies, 7(3), Art. 3. https://doi.org/10.46539/jfs.v7i3.436 (In Russian).

Awijen, H., Ben Zaied, Y., & Nguyen, D. K. (2022). Covid-19 vaccination, fear and anxiety: Evidence from Google search trends. Social Science & Medicine, 297, 114820. https://doi.org/10.1016/j.socscimed.2022.114820

Barbosa, M. T., Morais-Almeida, M., Sousa, C. S., & Bousquet, J. (2021). The “Big Five” Lung Diseases in CoViD-19 Pandemic – a Google Trends analysis. Pulmonology, 27(1), 71–72. https://doi.org/10.1016/j.pulmoe.2020.06.008

Burivalova, Z., Butler, R. A., & Wilcove, D. S. (2018). Analyzing Google search data to debunk myths about the public’s interest in conservation. Frontiers in Ecology and the Environment, 16(9), 509–514. https://doi.org/10.1002/fee.1962

Chang, Y.-W., Chiang, W.-L., Wang, W.-H., Lin, C.-Y., Hung, L.-C., Tsai, Y.-C., Suen, J.-L., & Chen, Y.-H. (2020). Google Trends-based non-English language query data and epidemic diseases: A cross-sectional study of the popular search behaviour in Taiwan. BMJ Open, 10(7), e034156. https://doi.org/10.1136/bmjopen-2019-034156

Chernenko, A. M., Agarkov, V. A., & Bronfman, S. A. (2022). Analysis of search queries as a tool for comparative assessment of the need for psychotherapeutic help. Psychology and Psycho-Techniques, 1, 67–79. https://doi.org/10.7256/2454-0722.2022.1.34873 (In Russian).

Guzman, G. (2011). Internet Search Behavior as an Economic Forecasting Tool: The Case of Inflation Expectations (SSRN Scholarly Paper No. 2004598). https://papers.ssrn.com/abstract=2004598

Ivanov, S., Dubravčíková, K., & Turcsányi, R. Q. (2020). Russian public opinion on China in the age of COVID-19. Central European Institute of Asian Studies. https://ceias.eu/wp-content/uploads/2021/08/RUS-poll-report.pdf

Lazareva, A. A. (2019). Dreaming Online: Search Queries as Reflections of Folklore. RSUH/RGGU Bulletin. “Literary Theory. Linguistics. Cultural Studies” Series, 4, 68–83. https://doi.org/10.28995/2686-7249-2019-4-68-83 (In Russian).

Li, X., Shang, W., Wang, S., & Ma, J. (2015). A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data. Electronic Commerce Research and Applications, 14(2), 112–125. https://doi.org/10.1016/j.elerap.2015.01.001

Markey, P. M., & Markey, C. N. (2013a). Seasonal Variation in Internet Keyword Searches: A Proxy Assessment of Sex Mating Behaviors. Archives of Sexual Behavior, 42(4), 515–521. https://doi.org/10.1007/s10508-012-9996-5

Markey, P. M., & Markey, C. N. (2013b). Annual variation in Internet keyword searches: Linking dieting interest to obesity and negative health outcomes. Journal of Health Psychology, 18(7), 875–886. https://doi.org/10.1177/1359105312445080

Petrova, D. A. (2019). Inflation forecasting based on internet queryingв. Economic development of Russia, 26(11), 55–62. (In Russian).

Raspopina, E. Y. (2011). Conceptual Systematics of Speech and Cognitive Strategies of Search Query Formulation in Internet Discourse. Bulletin of the Irkutsk State Linguistic University, 4, 121–128. (In Russian).

Romanova, A. P., Khlyscheva, E. V., Yakushenkov, S. N., & Topchiev, M. S. (2013). Alien and Cultural Security. ROSSPEN. (In Russian).

Scrutton Alvarado, N., & Stevenson, T. J. (2018). Appetitive information seeking behaviour reveals robust daily rhythmicity for Internet-based food-related keyword searches. Royal Society Open Science, 5(7), 172080. https://doi.org/10.1098/rsos.172080

Silaeva, V. L. (2015). Higher Education in the Perceptions of Internet Users (on the Example of Yandex Search Queries). Higher Education in Russia, 11, 47–52. (In Russian).

Thelwall, M. (2009). Introduction to Webometrics: Quantitative Web Research for the Social Sciences. Springer International Publishing. https://doi.org/10.1007/978-3-031-02261-6

Thelwall, M., Vaughan, L., & Björneborn, L. (2006). Webometrics. Annual Review of Information Science and Technology, 39(1), 81–135. https://doi.org/10.1002/aris.1440390110

Woo, S. E., Tay, L., & Proctor, R. W. (Eds.). (2020). Big data in psychological research. American Psychological Association.

Yang, S., Santillana, M., & Kou, S. C. (2015). Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences, 112(47), 14473–14478. https://doi.org/10.1073/pnas.1515373112

Zabokritskaya, L. D., & Oreshkina, T. A. (2021). Analysis of search query statistics as a tool for monitoring the ecological attitudes of the region's population. Vestnik Instituta Sociologii, 12(2), 175‑193. https://doi.org/10.19181/vis.2021.12.2.721 (In Russian).

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.