{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:46:50Z","timestamp":1760233610532,"version":"build-2065373602"},"reference-count":72,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:00:00Z","timestamp":1611792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Luxembourg\u2019s Fonds National de la Recherche","award":["COVID-19\/2020-1\/14700602 (PandemicGR)"],"award-info":[{"award-number":["COVID-19\/2020-1\/14700602 (PandemicGR)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.<\/jats:p>","DOI":"10.3390\/bdcc5010005","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T09:03:45Z","timestamp":1611824625000},"page":"5","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Exploratory Study of COVID-19 Information on Twitter in the Greater Region"],"prefix":"10.3390","volume":"5","author":[{"given":"Ninghan","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Sciences, Technology and Medicine, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1226-5597","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhong","sequence":"additional","affiliation":[{"name":"Faculty of Sciences, Technology and Medicine, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4521-4112","authenticated-orcid":false,"given":"Jun","family":"Pang","sequence":"additional","affiliation":[{"name":"Faculty of Sciences, Technology and Medicine, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg"},{"name":"Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C.M., Brugnoli, E., Schmidt, A.L., Zola, P., Zollo, F., and Scala, A. (2020). The COVID-19 Social Media Infodemic, Nature Publishing Group. Scientific Reports.","DOI":"10.1038\/s41598-020-73510-5"},{"key":"ref_2","unstructured":"Singh, L., Bansal, S., Bode, L., Budak, C., Chi, G., Kawintiranon, K., Padden, C., Vanarsdall, R., Vraga, E., and Wang, Y. (2020). A first look at COVID-19 information and misinformation sharing on Twitter. arXiv."},{"key":"ref_3","first-page":"26","article-title":"Using Twitter and web news mining to predict COVID-19 outbreak","volume":"13","author":"Jahanbin","year":"2020","journal-title":"Asian Pac. J. Trop. Med."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, C., and David, M.B. (2011, January 21\u201324). Collaborative topic modelling for recommending scientific articles. Proceedings of the 2011 International Conference on Knowledge Discovery and Data Mining (SIGKDD), San Diego, CA, USA.","DOI":"10.1145\/2020408.2020480"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Medford, R.J., Saleh, S.N., Sumarsono, A., Perl, T.M., and Lehmann, C.U. (2020). An \u201cInfodemic\u201d: Leveraging high-volume Twitter data to understand public sentiment for the COVID-19 outbreak. Open Forum Infect. Dis., 7.","DOI":"10.1101\/2020.04.03.20052936"},{"key":"ref_6","unstructured":"Sharma, K., Seo, S., Meng, C., Rambhatla, S., and Liu, Y. (2020). COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gupta, S., Nguyen, T.D., Rojas, F.L., Raman, S., Lee, B., Bento, A., Simon, K.I., and Wing, C. (2020). Tracking Public and Private Response to the COVID-19 Epidemic: Evidence from State and Local Government Actions, National Bureau of Economic Research. Technical report.","DOI":"10.3386\/w27027"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11220","DOI":"10.1073\/pnas.2005335117","article-title":"Evidence from Internet search data shows information-seeking responses to news of local COVID-19 cases","volume":"117","author":"Bento","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","unstructured":"Lopez, C.E., Vasu, M., and Gallemore, C. (2020). Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset. arXiv."},{"key":"ref_10","unstructured":"Thelwall, M., and Thelwall, S. (2020). Retweeting for COVID-19: Consensus building, information sharing, dissent, and lockdown life. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21484","DOI":"10.1073\/pnas.0906910106","article-title":"Multiscale mobility networks and the spatial spreading of infectious diseases","volume":"106","author":"Balcan","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1080\/02723638.2013.778572","article-title":"Relational cities: Doha, Panama City, and Dubai as 21st century entrep\u00f4ts","volume":"34","author":"Sigler","year":"2013","journal-title":"Urban Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1111\/tesg.12432","article-title":"Relational cities disrupted: reflections on the particular geographies of COVID-19 For small but global urbanisation in Dublin, Ireland, and Luxembourg City, Luxembourg","volume":"111","author":"Hesse","year":"2020","journal-title":"Tijdschr. Voor Econ. Soc. Geogr."},{"key":"ref_14","first-page":"65","article-title":"Challenges and obstacles in the production of cross-border territorial strategies: the example of the Greater Region","volume":"1","author":"Decoville","year":"2017","journal-title":"Trans. Assoc. Eur. Sch. Plan."},{"key":"ref_15","unstructured":"(2021, January 24). The Greater Region at a Glance. Available online: http:\/\/www.granderegion.net\/en\/The-Greater-Region-at-a-Glance."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1111\/j.1467-9574.1996.tb01482.x","article-title":"The concept of Ro in epidemic theory","volume":"50","author":"Heesterbeek","year":"1996","journal-title":"Stat. Neerl."},{"key":"ref_17","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (HLT), Minneapolis, MN, USA."},{"key":"ref_18","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"Trans. Intell. Syst. Technol."},{"key":"ref_20","unstructured":"Chen, E., Lerman, K., and Ferrara, E. (2020). COVID-19: The First Public Coronavirus Twitter Dataset. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"taaa031","DOI":"10.1093\/jtm\/taaa031","article-title":"The pandemic of social media panic travels faster than the COVID-19 outbreak","volume":"27","author":"Depoux","year":"2020","journal-title":"J. Travel Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e2353","DOI":"10.1136\/bmj.e2353","article-title":"Can Twitter predict disease outbreaks?","volume":"344","author":"Zorlu","year":"2012","journal-title":"Br. Med J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e19421","DOI":"10.2196\/19421","article-title":"Using reports of symptoms and diagnoses on social media to predict COVID-19 case counts in mainland China: observational infoveillance study","volume":"22","author":"Shen","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1038\/s41586-020-2404-8","article-title":"The effect of large-scale anti-contagion policies on the COVID-19 pandemic","volume":"584","author":"Hsiang","year":"2020","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1377\/hlthaff.2020.00608","article-title":"Strong social distancing measures in the United States reduced The COVID-19 Growth Rate","volume":"39","author":"Courtemanche","year":"2020","journal-title":"Health Aff."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dergiades, T., Milas, C., and Panagiotidis, T. (2020). Effectiveness of government policies in response to the COVID-19 outbreak. SSRN.","DOI":"10.2139\/ssrn.3602004"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e18897","DOI":"10.2196\/18897","article-title":"Conversations and medical news frames on Twitter: Infodemiological study on covid-19 in South Korea","volume":"22","author":"Park","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"011003","DOI":"10.7189\/jogh.10.0101003","article-title":"More effective strategies are required to strengthen public awareness of COVID-19: Evidence from Google trends","volume":"10","author":"Hu","year":"2020","journal-title":"J. Glob. Health"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.ijid.2020.04.033","article-title":"Association of the COVID-19 pandemic with Internet search volumes: A Google trendsTM Analysis","volume":"95","author":"Effenberger","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e19447","DOI":"10.2196\/19447","article-title":"Global sentiments surrounding the COVID-19 pandemic on Twitter: analysis of Twitter trends","volume":"6","author":"Lwin","year":"2020","journal-title":"JMIR Public Health Surveill."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Samuel, J., Ali, G., Rahman, M., Esawi, E., and Samuel, Y. (2020). COVID-19 public sentiment insights and machine learning for tweets classification. Information, 11.","DOI":"10.31234\/osf.io\/sw2dn"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e19016","DOI":"10.2196\/19016","article-title":"Top concerns of tweeters during the COVID-19 pandemic: infoveillance study","volume":"22","author":"Alhuwail","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zamani, M., Schwartz, H.A., Eichstaedt, J., Guntuku, S.C., Ganesan, A.V., Clouston, S., and Giorgi, S. (2020, January 20). Understanding weekly COVID-19 concerns through dynamic content-specific LDA topic modeling. Proceedings of the 4th Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), online.","DOI":"10.18653\/v1\/2020.nlpcss-1.21"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yan, X., Guo, J., Lan, Y., and Cheng, X. (2013, January 13\u201317). A biterm topic model for short texts. Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil.","DOI":"10.1145\/2488388.2488514"},{"key":"ref_35","first-page":"21","article-title":"Social Media Location Intelligence: The Next Privacy Battle-An ArcGIS add-in and Analysis of Geospatial Data Collected from Twitter. com","volume":"9","author":"Weidemann","year":"2013","journal-title":"Int. J. Geoinform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.tourman.2017.11.001","article-title":"Tourists\u2019 digital footprint in cities: Comparing Big Data sources","volume":"66","year":"2018","journal-title":"Tour. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hecht, B., Hong, L., Suh, B., and Chi, E.H. (2011, January 7\u201312). Tweets from Justin Bieber\u2019s heart: the dynamics of the location field in user profiles. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada.","DOI":"10.1145\/1978942.1978976"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1080\/00330124.2014.907699","article-title":"Where in the world are you? Geolocation and language identification in Twitter","volume":"66","author":"Graham","year":"2014","journal-title":"Prof. Geogr."},{"key":"ref_39","unstructured":"(2021, January 24). European Centre for Disease Prevention and Control. Available online: https:\/\/www.ecdc.europa.eu\/en."},{"key":"ref_40","unstructured":"(2021, January 24). Sciensano: Belgian Institute for Health. Available online: https:\/\/epistat.wiv-isp.be\/covid\/."},{"key":"ref_41","unstructured":"(2021, January 24). NPGEO Corona Hub 2020. Available online: https:\/\/npgeo-corona-npgeo-de.hub.arcgis.com\/."},{"key":"ref_42","unstructured":"(2021, January 24). Donn\u00e9es Hospitali\u00e8res Relatives \u00e0 l\u2019\u00e9pid\u00e9mie de COVID-19. Available online: https:\/\/www.data.gouv.fr\/fr\/datasets\/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bettencourt, L.M.L.M.A., and Ribeiro, R.M. (2008). Real time Bayesian estimation of the epidemic potential of emerging infectious diseases. PLoS ONE, 3.","DOI":"10.1371\/journal.pone.0002185"},{"key":"ref_44","unstructured":"(2021, January 24). A Collection of Work Related to COVID-19. Available online: https:\/\/github.com\/k-sys\/covid-19."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0140-6736(20)30260-9","article-title":"Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study","volume":"395","author":"Wu","year":"2020","journal-title":"Lancet"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shen, M., Peng, Z., Xiao, Y., and Zhang, L. (2020). Modelling the epidemic trend of the 2019 novel coronavirus outbreak in China. BioRxiv.","DOI":"10.1101\/2020.01.23.916726"},{"key":"ref_47","unstructured":"Walker, P., Whittaker, C., Watson, O., Baguelin, M., Ainslie, K., and Bhatia, S. (2021, January 24). The Global Impact of COVID-19 and Strategies for Mitigation and Suppression. Available online: https:\/\/www.imperial.ac.uk\/mrc-global-infectious-disease-analysis\/covid-19\/report-12-global-impact-covid-19\/."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2000058","DOI":"10.2807\/1560-7917.ES.2020.25.4.2000058","article-title":"Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020","volume":"25","author":"Riou","year":"2020","journal-title":"Eurosurveillance"},{"key":"ref_49","unstructured":"(2021, January 24). Statement on the First Meeting of the International Health Regulations (2005) Emergency Committee Regarding the Outbreak of Novel Coronavirus (2019-nCoV). Available online: https:\/\/www.who.int\/news\/item\/23-01-2020-statement-on-themeeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov)."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e21340","DOI":"10.2196\/21340","article-title":"Social media as an early proxy for social distancing indicated by the COVID-19 reproduction number: observational study","volume":"6","author":"Younis","year":"2020","journal-title":"JMIR Public Health Surveill."},{"key":"ref_51","unstructured":"Smith, M.C., Broniatowski, D.A., Paul, M.J., and Dredze, M. (2016, January 21\u201323). Towards real-time measurement of public epidemic awareness: Monitoring influenza awareness through twitter. Proceedings of the Spring Symposium on Observational Studies Through Social Media and Other Human-generated Content, Stanford, CA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"102364","DOI":"10.1016\/j.ipm.2020.102364","article-title":"Privacy concerns of the Australian My Health Record: Implications for other large-scale opt-out personal health records","volume":"57","author":"Pang","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_53","unstructured":"Kolini, F., and Janczewski, L. (2017, January 16\u201320). Clustering and topic modelling: A new approach for analysis of national cyber security strategies. Proceedings of the Pacific Asia Conference on Information Systems. Association For Information Systems, Langkawi, Malaysia."},{"key":"ref_54","unstructured":"Garbe, W. (2021, January 24). Python port of SymSpell. Available online: https:\/\/github.com\/mammothb\/symspellpy."},{"key":"ref_55","unstructured":"Sinka, M.P., and Corne, D.W. (2003, January 13\u201317). Towards modernised and web-specific stoplists for web document analysis. Proceedings of the IEEE\/WIC International Conference on Web Intelligence (WI), Halifax, NS, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e21559","DOI":"10.2196\/21559","article-title":"Analysis of scientific publications during the early phase of the COVID-19 pandemic: Topic modeling study","volume":"22","author":"Eriksson","year":"2020","journal-title":"J. Med Internet Res."},{"key":"ref_57","unstructured":"(2021, January 24). Contextual Topic Identification for Steam Reviews. Available online: https:\/\/github.com\/Stveshawn\/contextual_topic_identification."},{"key":"ref_58","unstructured":"Wagstaff, K., Cardie, C., Rogers, S., and Schr\u00f6dl, S. (July, January 28). Constrained k-means clustering with background knowledge. Proceedings of the 2001 International Conference on Machine Learning (ICML). Citeseer, Williamstown, MA, USA."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1162\/tacl_a_00082","article-title":"Detecting cross-cultural differences using a multilingual topic model","volume":"4","author":"Shutova","year":"2016","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_61","unstructured":"Ramos, J. (2003, January 21\u201324). Using TF-IDF to determine word relevance in document queries. Proceedings of the 2003 International Conference on Machine Learning (ICML), Washington, DC, USA."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5645","DOI":"10.1016\/j.eswa.2015.02.055","article-title":"An analysis of the coherence of descriptors in topic modelling","volume":"42","author":"Greene","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_63","unstructured":"Newman, D., Lau, J.H., Grieser, K., and Baldwin, T. (2010, January 2\u20134). Automatic evaluation of topic coherence. Proceedings of the 2010 Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL, Los Angeles, CA, USA."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Aranganayagi, S., and Thangavel, K. (2007, January 13\u201315). Clustering categorical data using silhouette coefficient as a relocating measure. Proceedings of the 2007 International Conference on Computational Intelligence and Multimedia Applications, Sivakasi, Tamil Nadu, India.","DOI":"10.1109\/ICCIMA.2007.328"},{"key":"ref_65","unstructured":"Liu, B., Li, X., Lee, W.S., and Yu, P.S. (2004, January 25\u201329). Text classification by labeling words. Proceedings of the AAAI, San Jose, CA, USA."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lee, K., Palsetia, D., Narayanan, R., Patwary, M.M.A., Agrawal, A., and Choudhary, A. (2011, January 11). Twitter trending topic classification. Proceedings of the 2011 IEEE International Conference on Data Mining Workshops (ICDM), Vancouver, BC, Canada.","DOI":"10.1109\/ICDMW.2011.171"},{"key":"ref_67","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_69","unstructured":"(2021, January 24). Total Population, Luxembourgers and Foreigners, of Usual Residence in Luxembourg. Available online: https:\/\/statistiques.public.lu\/stat\/TableViewer\/tableView.aspx?ReportId=12856."},{"key":"ref_70","first-page":"1","article-title":"Using social and behavioural science to support COVID-19 pandemic response","volume":"4","author":"Smith","year":"2020","journal-title":"Nat. Hum. Behav."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.cub.2011.10.030","article-title":"The optimism bias","volume":"21","author":"Sharot","year":"2011","journal-title":"Curr. Biol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1177\/1524839908322114","article-title":"Public support for government actions during a flu pandemic: lessons learned from a statewide survey","volume":"9","author":"Paek","year":"2008","journal-title":"Health Promot. 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