{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:37:56Z","timestamp":1760236676532,"version":"build-2065373602"},"reference-count":68,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T00:00:00Z","timestamp":1639353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan","award":["AP09259587"],"award-info":[{"award-number":["AP09259587"]}]},{"DOI":"10.13039\/501100005357","name":"Slovak Research and Development Agency","doi-asserted-by":"publisher","award":["APVV PP-COVID-20\u20130013"],"award-info":[{"award-number":["APVV PP-COVID-20\u20130013"]}],"id":[{"id":"10.13039\/501100005357","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics connected with the pandemic in the internet media of Kazakhstan and the Russian Federation. The main goal of the research is to propose a method that would make it possible to analyze the correlation between mass media dynamic indicators and the World Health Organization COVID-19 data. In order to solve the task, three approaches related to the representation of mass media dynamics in numerical form\u2014automatically obtained topics, average sentiment, and dynamic indicators\u2014were proposed and applied according to a manually selected list of search queries. The results of the analysis indicate similarities and differences in the ways in which the epidemiological situation is reflected in publications in Russia and in Kazakhstan. In particular, the publication activity in both countries correlates with the absolute indicators, such as the daily number of new infections, and the daily number of deaths. However, mass media tend to ignore the positive rate of confirmed cases and the virus reproduction rate. If we consider strictness of quarantine measures, mass media in Russia show a rather high correlation, while in Kazakhstan, the correlation is much lower. Analysis of search queries revealed that in Kazakhstan the problem of fake news and disinformation is more acute during periods of deterioration of the epidemiological situation, when the level of crime and poverty increase. The novelty of this work is the proposal and implementation of a method that allows the performing of a comparative analysis of objective COVID-19 statistics and several mass media indicators. In addition, it is the first time that such a comparative analysis, between different countries, has been performed on a corpus in a language other than English.<\/jats:p>","DOI":"10.3390\/computation9120140","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T01:20:41Z","timestamp":1639444841000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Mass Media as a Mirror of the COVID-19 Pandemic"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7378-9212","authenticated-orcid":false,"given":"Kirill","family":"Yakunin","sequence":"first","affiliation":[{"name":"Institute of Information and Computational Technologies MES RK, Pushkin Street, 125, Almaty 050010, Kazakhstan"},{"name":"Institute of Automation and Information Technologies, Satbayev University, Satpayev Street, 22A, Almaty 050013, Kazakhstan"},{"name":"School of Engineering Management, Almaty Management University, Rozybakiev Street, 227, Almaty 050060, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3727-043X","authenticated-orcid":false,"given":"Ravil I.","family":"Mukhamediev","sequence":"additional","affiliation":[{"name":"Institute of Information and Computational Technologies MES RK, Pushkin Street, 125, Almaty 050010, Kazakhstan"},{"name":"Institute of Automation and Information Technologies, Satbayev University, Satpayev Street, 22A, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9087-0311","authenticated-orcid":false,"given":"Elena","family":"Zaitseva","sequence":"additional","affiliation":[{"name":"Faculty of Management Science and Informatics, University of Zilina, Univerzitn\u00e1 8215\/1, 010 26 \u017dilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vitaly","family":"Levashenko","sequence":"additional","affiliation":[{"name":"Faculty of Management Science and Informatics, University of Zilina, Univerzitn\u00e1 8215\/1, 010 26 \u017dilina, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4203-800X","authenticated-orcid":false,"given":"Marina","family":"Yelis","sequence":"additional","affiliation":[{"name":"Institute of Information and Computational Technologies MES RK, Pushkin Street, 125, Almaty 050010, Kazakhstan"},{"name":"Institute of Automation and Information Technologies, Satbayev University, Satpayev Street, 22A, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9974-3215","authenticated-orcid":false,"given":"Adilkhan","family":"Symagulov","sequence":"additional","affiliation":[{"name":"Institute of Information and Computational Technologies MES RK, Pushkin Street, 125, Almaty 050010, Kazakhstan"},{"name":"Institute of Automation and Information Technologies, Satbayev University, Satpayev Street, 22A, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-9071","authenticated-orcid":false,"given":"Yan","family":"Kuchin","sequence":"additional","affiliation":[{"name":"Institute of Information and Computational Technologies MES RK, Pushkin Street, 125, Almaty 050010, Kazakhstan"},{"name":"Institute of Automation and Information Technologies, Satbayev University, Satpayev Street, 22A, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Muhamedijeva","sequence":"additional","affiliation":[{"name":"Institute of Information and Computational Technologies MES RK, Pushkin Street, 125, Almaty 050010, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margulan","family":"Aubakirov","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Maharishi International University, 1000 N 4th Street, Fairfield, IA 52557, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viktors","family":"Gopejenko","sequence":"additional","affiliation":[{"name":"International Radio Astronomy Centre, Ventspils University of Applied Sciences, Inzhenieru Street, 101, LV-3601 Ventspils, Latvia"},{"name":"Department of Natural Science and Computer Technologies, ISMA University, Lomonosov Street, 1, LV-1011 Riga, Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,13]]},"reference":[{"key":"ref_1","unstructured":"Baldwin, R., and di Mauro, B.W. (2020). Economics in the time of COVID-19: A new eBook. VOX CEPR Policy Portal, 2\u20133."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/S0140-6736(14)62254-6","article-title":"Transitioning health systems for multimorbidity","volume":"386","author":"Atun","year":"2015","journal-title":"Lancet"},{"key":"ref_3","first-page":"70","article-title":"The category of effectiveness in the health care system","volume":"10","author":"Orlov","year":"2010","journal-title":"Basic Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"020303","DOI":"10.7189\/jogh.08.020303","article-title":"Artificial intelligence, machine learning and health systems","volume":"8","author":"Panch","year":"2018","journal-title":"J. Glob. Health"},{"key":"ref_5","unstructured":"(2021, September 10). The Socio-Economic Impact of AI in Healthcare, Available online: https:\/\/www.medtecheurope.org\/wp-content\/uploads\/2020\/10\/mte-ai_impact-in-healthcare_oct2020_report.pdf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mukhamediev, R.I., Symagulov, A., Kuchin, Y., Yakunin, K., and Yelis, M. (2021). From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review. Appl. Sci., 11.","DOI":"10.3390\/app11125541"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_8","unstructured":"Daniel, J., Willie, R., and Copley, C. (August, January 28). Towards automating healthcare question answering in a noisy multilingual low-resource setting. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_9","unstructured":"Feng, L., Xiaoli, W., Qingfeng, W., Jiaying, L., Xueliang, Q., and Zhifeng, B. (2020). HQADeepHelper: A deep learning system for healthcare question answering. Companion Proceedings of the Web Conference 2020 (WWW \u201920), Association for Computing Machinery."},{"key":"ref_10","unstructured":"Draganescu, O. (2019). Forms of Influencing Young People through Media Discourse. EIRP Proc., 13, Available online: http:\/\/www.proceedings.univ-danubius.ro\/index.php\/eirp\/article\/view\/1965\/2250."},{"key":"ref_11","first-page":"398","article-title":"Role of mass media in shaping public opinion","volume":"XI","author":"Choudhary","year":"2020","journal-title":"Aut Aut Res. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3961\/jpmph.20.094","article-title":"Impact of Rumors and Misinformation on COVID-19 in Social Media","volume":"53","author":"Tasnim","year":"2020","journal-title":"J. Prev. Med. Public Health"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Wang, Y., Fu, H., and Dai, J. (2020). Mental health problems and social media exposure during COVID-19 outbreak. PLoS ONE, 15.","DOI":"10.2139\/ssrn.3541120"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"36645","DOI":"10.1109\/ACCESS.2021.3062875","article-title":"Investigating COVID-19 News across Four Nations: A Topic Modeling and Sentiment Analysis Approach","volume":"9","author":"Ghasiya","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mukhamediev, R.I., Yakunin, K., Mussabayev, R., Buldybayev, T., Kuchin, Y., Murzakhmetov, S., and Yelis, M. (2020). Classification of Negative Information on Socially Significant Topics in Mass Media. Symmetry, 12.","DOI":"10.3390\/sym12121945"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.procs.2020.11.022","article-title":"Propaganda Identification Using Topic Modelling","volume":"178","author":"Kirill","year":"2020","journal-title":"Proc. Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Battineni, G., Chintalapudi, N., and Amenta, F. (2020). Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model. Appl. Comput. Inform.","DOI":"10.1108\/ACI-09-2020-0059"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yakunin, K., Murzakhmetov, S., Mussabayev, R., and Muhamedyev, R. (2021, January 28\u201330). News popularity prediction using topic modelling. Proceedings of the 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), Nur-Sultan, Kazakhstan.","DOI":"10.1109\/SIST50301.2021.9465884"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tatar, A., Antoniadis, P., de Amorim, M.D., and Fdida, S. (2012, January 26\u201329). Ranking news articles based on popularity prediction. Proceedings of the 2012 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, Istanbul, Turkey.","DOI":"10.1109\/ASONAM.2012.28"},{"key":"ref_20","unstructured":"Bandari, R., Asur, S., and Huberman, B. (2012, January 4\u20137). The pulse of news in social media: Forecasting popularity. Proceedings of the International AAAI Conference on Web and Social Media, Dublin, Ireland."},{"key":"ref_21","unstructured":"(2021, August 05). Edelman Trust Barometer. Available online: https:\/\/www.edelman.com\/trust-barometer."},{"key":"ref_22","unstructured":"Briggs, A., and Cobley, P. (1998). Promotional strategies and media power. Introduction to Media, Longman."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bushman, B., and Whitaker, J. (2012). Media influence on behavior. Encyclopedia of Human Behavior, Elsevier Inc.. [2nd ed.].","DOI":"10.1016\/B978-0-12-375000-6.00386-4"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Stacks, D., Li, Z.C., and Spaulding, C. (2015). Media effects. International Encyclopedia of the Social & Behavioral Sciences, Elsevier Inc.. [2nd ed.].","DOI":"10.1016\/B978-0-08-097086-8.95045-1"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cogsys.2018.12.018","article-title":"Human-machine interaction: A case study on fake news detection using a backtracking based on a cognitive system","volume":"55","author":"Ko","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bushman, B.J., and Whitaker, J.L. (2017). Media Influence on Behavior. Reference Module in Neuroscience and Biobehavioral Psychology, Elsevier Inc.","DOI":"10.1016\/B978-0-12-809324-5.06481-6"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"110962","DOI":"10.1016\/j.paid.2021.110962","article-title":"A neglected reality of mass media during COVID-19: Effect of pandemic news on individual\u2019s positive and negative emotion and psychological resilience","volume":"180","author":"Giri","year":"2021","journal-title":"Personal. Individ. Differ."},{"key":"ref_28","first-page":"1","article-title":"Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak","volume":"7","author":"Aslam","year":"2020","journal-title":"Human. Soc. Sci. Commun."},{"key":"ref_29","first-page":"171","article-title":"How people emotionally respond to the news on COVID-19: An online survey","volume":"11","author":"Hamidein","year":"2020","journal-title":"Basic Clin. Neurosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"425","DOI":"10.3389\/fpubh.2020.00425","article-title":"Political Consequences of COVID-19 and Media Framing in South Korea","volume":"8","author":"Jo","year":"2020","journal-title":"Front. Public Health"},{"key":"ref_31","first-page":"100021","article-title":"Leveraging Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak in Singapore","volume":"1","author":"Ridhwan","year":"2021","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"ref_32","first-page":"e290223","article-title":"Impact of Covid-19 on the media system. Communicative and democratic consequences of news consumption during the outbreak","volume":"29","year":"2020","journal-title":"Prof. Inf."},{"key":"ref_33","first-page":"178","article-title":"Tell me who your sources are: Perceptions of news credibility on social media","volume":"13","author":"Tandoc","year":"2019","journal-title":"J. Pract."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Song, X., Petrak, J., Jiang, Y., Singh, I., Maynard, D., and Bontcheva, K. (2021). Classification aware neural topic model for COVID-19 disinformation categorisation. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247086"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e201","DOI":"10.1016\/S2589-7500(20)30026-1","article-title":"Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: A population-level observational study","volume":"2","author":"Sun","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gabrielyan, D., Masso, J., and Uuskula, L. (2020, January 8\u20139). Mining news data for the measurement and prediction of inflation expectations. Proceedings of the CARMA 2020\u20143rd International Conference on Advanced Research Methods and Analytics, Valencia, Spain.","DOI":"10.4995\/CARMA2020.2020.11322"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Leombroni, M., Vedolin, A., Venter, G., and Whelan, P. (2021). Central bank communication and the yield curve. J. Financ. Econ.","DOI":"10.1016\/j.jfineco.2021.04.036"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"161","DOI":"10.15514\/ISPRAS-2017-29(2)-6","article-title":"Review and experimental comparison of text clustering methods","volume":"29","author":"Parkhomenko","year":"2017","journal-title":"Proc. Inst. Syst. Program. RAS"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Vorontsov, K., Frei, O., Apishev, M., Romov, P., and Dudarenko, M. (2015, January 9\u201311). Bigartm: Open source library for regularized multimodal topic modeling of large collections. Proceedings of the International Conference on Analysis of Images, Social Networks and Texts, Yekaterinburg, Russia.","DOI":"10.1007\/978-3-319-26123-2_36"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","article-title":"Latent Dirichlet allocation (LDA) and topic modeling: Models, applications, a survey","volume":"78","author":"Jelodar","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alsolamy, M., Alotaibi, A., Alabbas, A., and Abdullah, M. (2021). Topic based Sentiment Analysis for COVID-19 Tweets. Int. J. Adv. Comput. Sci. Appl., 12.","DOI":"10.14569\/IJACSA.2021.0120172"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., and Zhu, T. (2020). Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0239441"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tao, G., Miao, Y., and Ng, S. (2020, January 7\u201311). COVID-19 topic modeling and visualization. Proceedings of the 2020 24th International Conference Information Visualisation (IV), Melbourne, Australia.","DOI":"10.1109\/IV51561.2020.00129"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mutanga, M.B., and Abayomi, A. (2020). Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach. Afr. J. Sci. Technol. Innov. Dev., 1\u201310.","DOI":"10.1080\/20421338.2020.1817262"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e175","DOI":"10.1016\/S2589-7500(20)30315-0","article-title":"What social media told us in the time of COVID-19: A scoping review","volume":"3","author":"Tsao","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kuchler, T., Russel, D., and Stroebel, J. (2021). JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook. J. Urban Econ., 103314.","DOI":"10.1016\/j.jue.2020.103314"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"686720","DOI":"10.3389\/fdgth.2021.686720","article-title":"Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA","volume":"3","author":"Gupta","year":"2021","journal-title":"Front Digit Health"},{"key":"ref_48","unstructured":"Angelov, D. (2020). Top2vec: Distributed representations of topics. arXiv."},{"key":"ref_49","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"329","DOI":"10.3390\/idr13020032","article-title":"Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models","volume":"13","author":"Chintalapudi","year":"2021","journal-title":"Infect. Dis. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chakraborty, A.K., Das, S., and Kolya, A.K. (2021). Sentiment analysis of covid-19 tweets using evolutionary classification-based LSTM model. Proceedings of Research and Applications in Artificial Intelligence, Springer.","DOI":"10.1007\/978-981-16-1543-6_7"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","article-title":"An interactive web-based dashboard to track COVID-19 in real time","volume":"20","author":"Dong","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yakunin, K., Kalimoldayev, M., Mukhamediev, R., Mussabayev, R., Barakhnin, V., Kuchin, Y., Murzakhmetov, S., Buldybayev, T., Ospanova, U., and Yelis, M. (2021). KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus. Data, 6.","DOI":"10.3390\/data6030031"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Arroyo-Marioli, F., Bullano, F., Kucinskas, S., and Rond\u00f3n-Moreno, C. (2021). Tracking R of COVID-19: A new real-time estimation using the Kalman filter. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0244474"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"693","DOI":"10.20537\/2076-7633-2012-4-4-693-706","article-title":"Regularization, robustness and sparsity of probabilistic thematic models","volume":"4","author":"Vorontsov","year":"2012","journal-title":"Comput. Res. Model."},{"key":"ref_56","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_57","unstructured":"Mimno, D., Wallach, H., Talley, E., Leenders, M., and McCallum, A. (2011, January 27\u201331). Optimizing semantic coherence in topic models. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK."},{"key":"ref_58","first-page":"273","article-title":"A fast morphological algorithm with unknown word guessing induced by a dictionary for a web search engine","volume":"2003","author":"Segalovich","year":"2003","journal-title":"MLMTA"},{"key":"ref_59","unstructured":"(2021, November 13). Stop-Words 2018.7.23. Available online: https:\/\/pypi.org\/project\/stop-words\/."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"416","DOI":"10.3390\/make1010025","article-title":"The number of topics optimization: Clustering approach","volume":"1","author":"Krasnov","year":"2019","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.cmrp.2020.03.011","article-title":"Effects of COVID-19 pandemic in daily life","volume":"10","author":"Haleem","year":"2020","journal-title":"Curr. Med. Res. Pract."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Fernandes, N. (2021, December 04). Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy. 2020. SSRN 3557504. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3557504.","DOI":"10.2139\/ssrn.3557504"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e426","DOI":"10.1097\/JOM.0000000000002236","article-title":"Work from Home During the COVID-19 Outbreak: The Impact on Employees\u2019 Remote Work Productivity, Engagement, and Stress","volume":"63","author":"Galanti","year":"2021","journal-title":"J. Occup. Environ. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1007\/s12103-020-09578-6","article-title":"Exploring the immediate effects of COVID-19 containment policies on crime: An empirical analysis of the short-term aftermath in Los Angeles","volume":"46","author":"Campedelli","year":"2021","journal-title":"Am. J. Crim. Justice"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"101475","DOI":"10.1016\/j.tele.2020.101475","article-title":"Fake news and COVID-19: Modelling the predictors of fake news sharing among social media users","volume":"56","author":"Apuke","year":"2021","journal-title":"Telemat. Inform."},{"key":"ref_66","first-page":"171","article-title":"ElasticSearch: An advanced and quick search technique to handle voluminous data","volume":"2","author":"Divya","year":"2013","journal-title":"Compusoft"},{"key":"ref_67","unstructured":"Bia\u0142ecki, A., Muir, R., and Ingersoll, G. (2012, January 16). Apache lucene. Proceedings of the SIGIR 2012 Workshop on Open Source Information Retrieval, Portland, OR, USA."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"60","DOI":"10.17323\/1998-0663.2019.4.60.72","article-title":"The design of the structure of the software system for processing text document corpus","volume":"13","author":"Barakhnin","year":"2019","journal-title":"Bus. Inform."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/9\/12\/140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:46:59Z","timestamp":1760168819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/9\/12\/140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,13]]},"references-count":68,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["computation9120140"],"URL":"https:\/\/doi.org\/10.3390\/computation9120140","relation":{},"ISSN":["2079-3197"],"issn-type":[{"type":"electronic","value":"2079-3197"}],"subject":[],"published":{"date-parts":[[2021,12,13]]}}}