References
- Aslam, F., Awan, T. M., Syed, J. H., Kashif, A., & Parveen, M. (2020). Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanities and Social Sciences Communications, 7(1), 1–12. https://doi.org/10.1057/s41599-020-0523-3
- Atun, R. (2015). Transitioning health systems for multimorbidity. Lancet (London, England), 386(9995), 721–722. https://doi.org/10.1016/S0140-6736(14)62254-6
- Baba, C., M Cherecheş, R., & Mosteanu, O. (2017). The mass media influence on the impact of health policy. Transylvanian Review of Administrative Sciences, 3(19), 15–20.
- Baldwin, R., & Weder di Mauro, B. (2020). Economics in the time of COVID-19. CEPR Press VoxEU.org eBook.
- Barakhnin, V. B., Duisenbayeva, A. N., Kozhemyakina, O. Y., Yergaliyev, Y. N., & Muhamedyev, R. I. (2018). The automatic processing of the texts in natural language: Some bibliometric indicators of the current state of this research area. Journal of Physics: Conference Series, 1117(1), 012001.
- Barakhnin, V., Kozhemyakina, O., Mukhamedyev, R., Borzilova, Y., & Yakunin, K. (2019). The design of the structure of the software system for processing text document corpus. Business Informatics, 13(4), 60–72. https://doi.org/10.17323/2587-814X.2019.1.60.72
- Barysevich, A. (2019). 20 of the best social media monitoring tools to consider. Social Media Today. https://www.socialmediatoday.com/news/20-of-the-best-social-media-monitoring-tools-to-consider/545036/
- Bauer, M. W., & Suerdem, A. (2016). Developing science culture indicators through text mining and online media monitoring. In OECD Blue Sky Forum on Science and Innovation Indicators; LSE Research (Eds.), Proceedings of the conference held in Ghent (pp. 19–21).
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.
- Bou-Karroum, L., El-Jardali, F., Hemadi, N., Faraj, Y., Ojha, U., Shahrour, M., Darzi, A., Ali, M., Doumit, C., Langlois, E. V., Melki, J., AbouHaidar, G. H., & Akl, E. A. (2017). Using media to impact health policy-making: An integrative systematic review. Implementation Science, 12(1), 52–66. https://doi.org/10.1186/s13012-017-0581-0
- Bushman, B. J., & Whitaker, J. (2012). Media influence on behavior. In Encyclopedia of Human Behavior (2nd ed., 571–575).
- Bushman, B. J., & Whitaker, J. L. (2012). Media influence on behavior. In V. S. Ramachandran (Ed.), Encyclopedia of human behavior (2nd ed., pp. 571–575). Elsevier Inc.
- Casero-Ripolles, A. (2020). Impact of COVID-19 on the media system: Communicative and democratic consequences of news consumption during the outbreak. El Profesional De La Información, 29(2). e-ISSN: 1699–2407. https://doi.org/10.3145/epi.2020.mar.23
- Dieng, A. B., Ruiz, F. J., & Blei, D. M. (2020). Topic modeling in embedding spaces. Transactions of the Association for Computational Linguistics, 8, 439–453. https://doi.org/10.1162/tacl_a_00306
- Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet. Infectious Diseases, 20(5), 533–534. https://doi.org/10.1016/S1473-3099(20)30120-1
- Dubey, A. D. (2020). Twitter sentiment analysis during covid19 outbreak. SSRN Electronic Journal, https://doi.org/10.2139/ssrn.3572023
- Erzurumlu, S. S., & Pachamanova, D. (2020). Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations. Technological Forecasting and Social Change, 156, 120041. https://doi.org/10.1016/j.techfore.2020.120041
- Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Wang, Y., Fu, H., & Dai, J. (2020). Mental health problems and social media exposure during COVID-19 outbreak. PloS One, 15(4), e0231924. https://doi.org/10.1371/journal.pone.0231924
- Giri, S. P., & Maurya, A. K. (2021). A neglected reality of mass media during COVID-19: Effect of pandemic news on individuals’ positive and negative emotion and psychological resilience. Personality and Individual Differences, 180, 110962. https://doi.org/10.1016/j.paid.2021.110962
- Hamidein, Z., Hatami, J., & Rezapour, T. (2020). How people emotionally respond to the news on COVID-19: An online survey. Basic and Clinical Neuroscience, 11(2), 171–178. https://doi.org/10.32598/bcn.11.covid19.809.2
- Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2018). Latent Dirichlet Allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 77(24), 1–43. https://doi.org/10.1007/s11042-018-6611-0
- Jo, W., & Chang, D. (2020). Political consequences of COVID-19 and media framing in South Korea. Frontiers in Public Health, 8, 572584. https://doi.org/10.3389/fpubh.2020.572584
- Kirill, Y., Mihail, I. G., Sanzhar, M., Rustam, M., Olga, F., & Ravil, M. (2020). Propaganda identification using topic modelling. Procedia Computer Science, 178, 205–212. https://doi.org/10.1016/j.procs.2020.02.106
- Krasnov, F., & Sen, A. (2019). The number of topics optimization: Clustering approach. Machine Learning and Knowledge Extraction, 1(1), 416–426. https://doi.org/10.3390/make1030031
- Li, X., Zhou, M., Wu, J., Yuan, A., Wu, F., & Li, J. (2020). Analyzing COVID-19 on online social media: Trends, sentiments and emotions. ArXiv, abs/2005.14464.
- Mashechkin, I., Petrovsky, M., & Tsarev, D. (2013). Methods for calculating the relevance of text fragments based on topic models in the problem of automatic annotation. Computational Methods and Programming, 14(1), 91–102.
- Mheidly, N., & Fares, J. (2020). Leveraging media and health communication strategies to overcome the COVID-19 infodemic. Journal of Public Health Policy, 41(4), 410–420. https://doi.org/10.1057/s41271-020-00247-w
- Miller, D. (1998). Promotional strategies and media power. In A. Briggs & P. Cobley (Eds.), The media: An introduction (pp. 65–80). Longman.
- Mohamed Ridhwan, K., & Hargreaves, C. A. (2021). Leveraging twitter data to understand public sentiment for the COVID‐19 outbreak in Singapore. International Journal of Information Management Data Insights, 1(2), 100021. https://doi.org/10.1016/j.ijimdi.2021.100021
- Mukhamediev, R. I., Yakunin, K., Mussabayev, R., Buldybayev, T., Kuchin, Y., Murzakhmetov, S., & Yelis, M. (2020). Classification of negative information on socially significant topics in mass media. Symmetry, 12(12), 1945. https://doi.org/10.3390/sym12121945
- Nemes, L, & Kiss, A. (2020). Social media sentiment analysis based on Covid-19. Journal of Information and Telecommunication, 5(1), 1–15. https://doi.org/10.1080/24751839.2020.1790793
- On Public Health and Healthcare System. (n.d). Retrieved February 27, 2023, from https://www.adilet.zan.kz/eng/docs/K2000000360
- Parhomenko, P. A., Grigorev, A. A., & Astrakhantsev, N. A. (2017). A survey and an experimental comparison of methods for text clustering: Application to scientific articles. Proceedings of the Institute for System Programming of the RAS, 29(2), 161–200. https://doi.org/10.15514/ISPRAS-2017-29(2)-11
- Pichai, S. (2023, February 24). COVID-19: How we’re continuing to help. Google Blog. https://blog.google/inside-google/company-announcements/covid-19-how-were-continuing-to-help/
- Rajendra Prasad, K., Mohammed, M., & Noorullah, R. M. (2019). Visual topic models for healthcare data clustering. Evolutionary Intelligence, 14(2), 545–562. https://doi.org/10.1007/s12065-019-00300-y
- Sadovskaya, L., Mukhamediev, R., Kosyakov, D., & Guskov, A. (2021). Natural language text processing: a review of publications. Artificial Intelligence and Decision Making, 10, 2552–2577.
- Shen, B., Guan, T., Ma, J., Yang, L., & Liu, Y. (2021). Social network research hotspots and trends in public health: A bibliometric and visual analysis. Public Health in Practice (Oxford, England), 2, 100155. https://doi.org/10.1016/j.puhip.2021.100155
- Song, X., Petrak, J., Jiang, Y., Singh, I., Maynard, D., & Bontcheva, K. (2021). Classification aware neural topic model for COVID-19 disinformation categorisation. PloS One, 16(2), e0245986. https://doi.org/10.1371/journal.pone.0247086
- Stacks, D. W., Cathy Li, Z., & Spaulding, C. (2015). Media effects. International Encyclopedia of the Social & Behavioral Sciences, 29–34.
- Tandoc, E. C. (2018). Tell me who your sources are. Journalism Practice, 13(2), 178–190. https://doi.org/10.1080/17512786.2017.1423237
- Tasnim, S., Hossain, M. M., & Mazumder, H. (2020). Impact of rumors and misinformation on COVID-19 in social media. Journal of Preventive Medicine and Public Health = Yebang Uihakhoe Chi, 53(3), 171–174. https://doi.org/10.3961/jpmph.20.094
- Vayansky, I., & Kumar, S. A. P. (2020). A review of Topic modeling methods. Information Systems, 94, 101582. https://doi.org/10.1016/j.is.2019.101582
- Vorontsov, K. V., & Potapenko, A. A. (2012). Regularization, robustness and sparsity of probabilistic topic models. Computer Research and Modeling, 4(4), 693–706. (Russian) https://www.elibrary.ru/item.asp?id=17786186; https://doi.org/10.20537/2076-7633-2012-4-4-693-706
- Vorontsov, K., Frei, O., Apishev, M., Romov, P., & Dudarenko, M. (2015). BigARTM: Open Source Library for regularized Multimodal topic modeling of large collections. Communications in Computer and Information Science, 535, 370–381. https://doi.org/10.1007/978-3-319-26123-6_32
- Yakunin, K., Kalimoldayev, M., Mukhamediev, R. I., Mussabayev, R., Barakhnin, V., Kuchin, Y., Murzakhmetov, S., Buldybayev, T., Ospanova, U., Yelis, M., Zhumabayev, A., Gopejenko, V., Meirambekkyzy, Z., & Abdurazakov, A. (2021). KazNews-Dataset: Single country overall digital mass media publication corpus. Data, 6(3), 31. https://doi.org/10.3390/data6030031
- Yakunin, K., Mukhamediev, R. I., Yelis, M., Kuchin, Y., Symagulov, A., Levashenko, V., Zaitseva, E., Aubakirov, M., Yunicheva, N., Muhamedijeva, E., Gopejenko, V., & Popova, Y. (2022). Analysis of the correlation between mass-media publication activity and COVID-19 epidemiological situation in early 2022. Information, 13(9), 434. https://doi.org/10.3390/info13030434
- Yakunin, K., Mukhamediev, R. I., Zaitseva, E., Levashenko, V., Yelis, M., Symagulov, A., Kuchin, Y., Muhamedijeva, E., Aubakirov, M., & Gopejenko, V. (2021). Mass media as a mirror of the COVID-19 pandemic. Computation, 9(12), 140. https://doi.org/10.3390/computation9120140
- Yakunin, K., Mukhamediev, R., Kuchin, Y., Musabayev, R., Buldybayev, T., & Murzakhmetov, S. (2021). Classification of negative publication in mass media using topic modeling. Journal of Physics: Conference Series, 1727(1), 012019. https://doi.org/10.1088/1742-6596/1727/1/012019
- Yin, H., Song, X., Yang, S, & Li, J. (2022). Sentiment analysis and topic modeling for COVID-19 vaccine discussions. World Wide Web, 25(3), 1067–1083. https://doi.org/10.1007/s11280-022-01029-y
- Yin, H., Yang, S, & Li, J. (2020). Detecting topic and sentiment dynamics due to Covid-19 pandemic using social media. Advanced Data Mining and Applications, 25(3), 610–623. https://doi.org/10.1007/978-3-030-65390-3_46
- Zhou, J., Zogan, H., Yang, S., Jameel, S., Xu, G., & Chen, F. (2021). Detecting community depression dynamics due to Covid-19 pandemic in Australia. IEEE Transactions on Computational Social Systems, 8(4), 982–991. https://doi.org/10.1109/tcss.2020.3047604