Acta Informatica Pragensia 2020, 9(2), 154-169 | DOI: 10.18267/j.aip.1386154

The Role of Twitter During the COVID-19 Crisis: A Systematic Literature Review

Mahsa Dalili Shoaei ORCID...1, Meisam Dastani ORCID...2
1 Ferdows School of Paramedical and Health, Birjand University of Medical Sciences, Birjand, Iran
2 Gonabad University of Medical Sciences, Gonabad, Iran

At the end of 2019, COVID-19 (Coronavirus 2019) emerged in Wuhan, China, and spread rapidly worldwide. The use of virtual social networks, especially Twitter, has increased due to the present condition. The purpose of the present systematic literature review is to review the investigations on Twitter's role in the COVID-19 crisis. For this purpose, an appropriate search strategy was used to extract the studies conducted in the Web of Science and PubMed databases. In the end, 24 articles were reviewed. The results indicate that in the period of the COVID-19 pandemic, the content and tweets posted on Twitter were affected by this crisis, and various people such as the general public, health professionals, and politicians were sharing opinions, emotions, personal experience, and educational content about exposure to COVID-19 on this social media. Therefore, the speed of providing information to people has been one of the main advantages of Twitter during the crisis of COVID-19; however, the risk of using invalid information without scientific citation is also one of the most important concerns of using Twitter among people as well as health and governmental organizations. Thus, users should evaluate information accuracy more carefully and pay attention to the quality and validity of information before employing or sharing it. Governments and professionals can also prevent this disease's contagion even in similar future crises by employing Twitter correctly in the period of crisis and using the useful experience gained from applying social networks in the outbreak of COVID-19.

Keywords: COVID-19, Crisis, Pandemic, Review, Role, Social media, Twitter

Received: August 30, 2020; Revised: September 26, 2020; Accepted: September 29, 2020; Prepublished online: September 29, 2020; Published: December 31, 2020  Show citation

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Dalili Shoaei, M., & Dastani, M. (2020). The Role of Twitter During the COVID-19 Crisis: A Systematic Literature Review. Acta Informatica Pragensia, 9(2), 154-169. doi: 10.18267/j.aip.138
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    References

    1. Abbas, M. A., Eliyana, A., Ekowati, D., Saud, M. M., Raza, M. A., & Wardani, M. R. (2020). Data set on coping strategies in the digital age: The role of psychological well-being and social capital among university students in Java Timor, Surabaya, Indonesia. Data in Brief, 30, 105583. https://doi.org/10.1016/j.dib.2020.105583 Go to original source...
    2. Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hamdi, M., & Shah, Z. (2020). Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study. Journal of Medical Internet Research, 22(4), e19016. https://doi.org/10.2196/19016 Go to original source...
    3. Aguilar-Gallegos, N., Romero-García, L. E., Martínez-González, E. G., García-Sánchez, E. I., & Aguilar-Ávila, J. (2020). Dataset on dynamics of Coronavirus on Twitter. Data in Brief, 30, 105684. https://doi.org/10.1016/j.dib.2020.105684 Go to original source...
    4. Allcott, H., Gentzkow, M., & Yu, C. (2019). Trends in the diffusion of misinformation on social media. Research & Politics, 6(2), 1-8. https://doi.org/10.1177/2053168019848554 Go to original source...
    5. Amani, F., Aghaie, B., Zeynizadeh, S., Tabrizian, S., Aslanian, R., & Jafarizadeh, R. (2020). Using social network rates among Ardabil city women over 25 years old. Journal of Human Sport and Exercise, 15(S429-S436). https://doi.org/10.14198/jhse.2020.15.Proc2.33 Go to original source...
    6. Auter, P. J., Douai, A., Makady, H., & West, C. (2016). Circulating health rumors in the 'Arab World': a 12-month content analysis of news stories and reader commentary about Middle East Respiratory Syndrome from two Middle Eastern news outlets. International Communication Gazette, 78(5), 411-431. https://doi.org/10.1177/1748048516640202 Go to original source...
    7. Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., & Chowell, G. (2020). A large-scale COVID-19 Twitter chatter dataset for open scientific research - An international collaboration. arXiv preprint arXiv:2004.03688. https://arxiv.org/abs/2004.03688 Go to original source...
    8. Brandt, H. M., Turner-McGrievy, G., Friedman, D. B., Gentile, D., Schrock, C., Thomas, T., & West, D. (2019). Examining the role of Twitter in response and recovery during and after historic flooding in South Carolina. Journal of Public Health Management and Practice, 25(5), E6-E12. https://doi.org/10.1097/PHH.0000000000000841 Go to original source...
    9. Barros, J. M., Duggan, J., & Rebholz-Schuhmann, D. (2020). The application of internet-based sources for public health surveillance (infoveillance): Systematic review. Journal of Medical Internet Research, 22(3), e13680. https://doi.org/10.2196/13680 Go to original source...
    10. Bisanzio, D., Kraemer, M. U., Bogoch, I. I., Brewer, T., Brownstein, J. S., & Reithinger, R. (2020). Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale. Geospatial Health, 15(1), 19-24. https://doi.org/10.4081/gh.2020.882 Go to original source...
    11. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210-230. https://doi.org/10.1111/j.1083-6101.2007.00393.x Go to original source...
    12. 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), e19301. https://doi.org/10.2196/19301 Go to original source...
    13. Chae, J., Thom, D., Jang, Y., Kim, S., Ertl, T., & Ebert, D. S. (2014). Public behavior response analysis in disaster events utilizing visual analytics of microblog data. Computers & Graphics, 38, 51-60. https://doi.org/10.1016/j.cag.2013.10.008 Go to original source...
    14. Chen, E., Lerman, K., & Ferrara, E. (2020a). Covid-19: The first public coronavirus twitter dataset. arXiv preprint arXiv:2003.07372. https://arxiv.org/abs/2003.07372
    15. Chen, E., Lerman, K., & Ferrara, E. (2020b). Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health Surveill, 6(2), e19273. https://doi.org/10.2196/19273 Go to original source...
    16. Dastani, M., Keramati, J., Poorfatemi, A., & Ekrami, A. (2015). The reasons and motives of virtual social networks among students of Gonabad University of Medical Sciences. Caspian Journal of Scientometrics, 2(2), 24-27. https://doi.org/10.22088/acadpub.BUMS.2.2.24 Go to original source...
    17. Dastani, M., Keramati, J., Poorfatemi, A., & Ekrami, A. (2016). The role of Social Networks on Academic Achievement of Gonabad University of Medical Science' students. Journal of Medical Education and Development, 11(2), 153-160.
    18. Dastani, M., Mohammadpour, A., & Bagheri, J. (2019). The Opportunities and Damages of Virtual Social Networks from Students' Perspectives; the Experience of Iranian Users. Library Philosophy and Practice, 2956. https://digitalcommons.unl.edu/libphilprac/2956/
    19. Dastani, M., & Ramezani, A. (2017). Role of Membership in Scientific and Specialized Groups of Virtual Social Networks in Increasing Knowledge, Professional Skills and E-Learning: A Case Study. International Research: Journal of Library and Information Science, 7(4), 595-602.
    20. Elwyn, G., Scholl, I., Tietbohl, C., Mann, M., Edwards, A. G., Clay, C., Légaré, F., Weijden, T. van der, Lewis, C. L., Wexler, R. M., & Frosch, D. L. (2013). "Many miles to go …": a systematic review of the implementation of patient decision support interventions into routine clinical practice. BMC Medical Informatics and Decision Making, 13(S2). https://doi.org/10.1186/1472-6947-13-s2-s14 Go to original source...
    21. Goff, D. A., Kullar, R., Laxminarayan, R., Mendelson, M., Nathwani, D., & Osterholm, M. (2019). Twitter to engage, educate, and advocate for global antibiotic stewardship and antimicrobial resistance. The Lancet Infectious Diseases, 19(3), 229-231. https://doi.org/10.1016/S1473-3099(19)30058-1 Go to original source...
    22. Golder, S., Klein, A., Magge, A., O'Connor, K., Cai, H., & Weissenbacher, D. (2020). Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK. medRxiv. https://doi.org/10.1101/2020.05.05.20083436 Go to original source...
    23. Goodchild, M. F., & Glennon, J. A. (2010). Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth, 3(3), 231-241. https://doi.org/10.1080/17538941003759255 Go to original source...
    24. Gui, X., Kou, Y., Pine, K. H., & Chen, Y. (2017). Managing uncertainty: using social media for risk assessment during a public health crisis. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 4520-4533). https://doi.org/10.1145/3025453.3025891 Go to original source...
    25. Houston, J. B., Hawthorne, J., Perreault, M. F., Park, E. H., Goldstein Hode, M., Halliwell, M. R., Turner McGowen, S. E., Davis, R., Vaid, S., McElderry, J. A., & Griffith, S. A. (2014). Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters, 39(1), 1-22. https://doi.org/10.1111/disa.12092 Go to original source...
    26. Hou, C., Chen, J., Zhou, Y., Hua, L., Yuan, J., He, S., Guo, Y., Zhang, S., Jia, Q., Zhao, C., Zhang, J., Xu, G., & Jia, E. (2020). The effectiveness of quarantine of Wuhan city against the Corona Virus Disease 2019 (COVID-19): A well-mixed SEIR model analysis. Journal of Medical Virology, 92(7), 841-848. https://doi.org/10.1002/jmv.25827 Go to original source...
    27. Jimenez-Sotomayor, M. R., Gomez-Moreno, C., & Soto-Perez-de-Celis, E. (2020). Coronavirus, Ageism, and Twitter: An Evaluation of Tweets about Older Adults and COVID-19. Journal of the American Geriatrics Society, 68(8), 1661-1665. https://doi.org/10.1111/jgs.16508 Go to original source...
    28. Kawchuk, G., Hartvigsen, J., Harsted, S., Nim, C. G., & Nyirö, L. (2020). Misinformation about spinal manipulation and boosting immunity: an analysis of Twitter activity during the COVID-19 crisis. Chiropractic & Manual Therapies, 28(1), 34. https://doi.org/10.1186/s12998-020-00319-4 Go to original source...
    29. Klein, A., Magge, A., O'Connor, K., Cai, H., Weissenbacher, D., & Gonzalez-Hernandez, G. (2020). A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter. medRxiv. https://doi.org/10.1101/2020.04.19.20069948 Go to original source...
    30. Kouzy, R., Abi Jaoude, J., Kraitem, A., El Alam, M. B., Karam, B., Adib, E., Zarka, J., Traboulsi, C., Akl, E., & Baddour, K. (2020). Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter. Cureus, 12(3), e7255. https://doi.org/10.7759/cureus.7255 Go to original source...
    31. Kostkova, P., De Quincey, E., & Jawaheer, G. (2010). The potential of social networks for early warning nad outbreak detection systems: the swine flu Twitter study. International Journal of Infectious Diseases, 14, e384-e385. https://doi.org/10.1016/j.ijid.2010.02.475 Go to original source...
    32. Kudchadkar, S. R., & Carroll, C. L. (2020). Using Social Media for Rapid Information Dissemination in a Pandemic. Pediatric Critical Care Medicine, Publish Ahead of Print. https://doi.org/10.1097/pcc.0000000000002474 Go to original source...
    33. Lee, H., & Oh, H. J. (2017). Normative mechanism of rumor dissemination on Twitter. Cyberpsychology, Behavior, and Social Networking, 20(3), 164-171. https://doi.org/10.1089/cyber.2016.0447 Go to original source...
    34. Kullar, R., Goff, D. A., Gauthier, T. P., & Smith, T. C. (2020). To Tweet or Not to Tweet-a Review of the Viral Power of Twitter for Infectious Diseases. Current Infectious Disease Reports, 22, Article 14. https://dx.doi.org/10.1007/s11908-020-00723-0 Go to original source...
    35. Leelawat, N., Tang, J., Saengtabtim, K., & Laosunthara, A. (2020). Trends of Tweets on the Coronavirus Disease-2019 (COVID-19) Pandemic. Journal of Disaster Research, 4(15), 530-533. Go to original source...
    36. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., Xing, X., Xiang, N., Wu, Y., Li, C., Chen, Q., Li, D., Liu, T., Zhao, J., Li, M., … Feng, Z. (2020). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. The New England Journal of Medicine, 382(13). https://doi.org/10.1056/NEJMoa2001316 Go to original source...
    37. Liu, Q., Gao, Y., & Chen, Y. (2014). Study on disaster information management system compatible with VGI and crowdsourcing. In The 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA). IEEE. https://doi.org/10.1109/WARTIA.2014.6976296 Go to original source...
    38. Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R., & Yang, Y. (2020). Global sentiments surrounding the COVID-19 pandemic on Twitter: analysis of Twitter trends. JMIR Public Health Surveill, 6(2), e19447. https://doi.org/10.2196/19447 Go to original source...
    39. Mackey, T., Purushothaman, V., Li, J., Shah, N., Nali, M., Bardier, C., … Cuomo, R. (2020). Machine Learning to Detect Self-Reporting of Symptoms, Testing access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study. JMIR Public Health Surveill, 6(2), e19509. https://doi.org/10.2196/19509 Go to original source...
    40. Madge, C., Meek, J., Wellens, J., & Hooley, T. (2009). Facebook, social integration and informal learning at university:'It is more for socialising and talking to friends about work than for actually doing work'. Learning, media and technology, 34(2), 141-155. https://doi.org/10.1080/17439880902923606 Go to original source...
    41. Norman, C. (2011). eHealth literacy 2.0: problems and opportunities with an evolving concept. Journal of Medical Internet Research, 13(4), e125. https://doi.org/10.2196/jmir.2035 Go to original source...
    42. Odlum, M., Cho, H., Broadwell, P., Davis, N., Patrao, M., Schauer, D., … Yoon, S. (2020). Application of Topic Modeling to Tweets as the Foundation for Health Disparity Research for COVID-19. In The Importance of Health Informatics in Public Health during a Pandemic, (pp. 24-27). https://doi.org/10.3233/SHTI200484 Go to original source...
    43. Odlum, M., & Yoon, S. (2015). What can we learn about the Ebola outbreak from tweets?. American journal of infection control, 43(6), 563-571. https://dx.doi.org/10.1016/j.ajic.2015.02.023 Go to original source...
    44. Panuganti, B. A., Jafari, A., MacDonald, B., & DeConde, A. S. (2020). Predicting COVID-19 Incidence Using Anosmia and Other COVID-19 Symptomatology: Preliminary Analysis Using Google and Twitter. Otolaryngology-Head and Neck Surgery, 163(3), 491-497. https://doi.org/10.1177/0194599820932128 Go to original source...
    45. Paré, G., & Kitsiou, S. (2017). Methods for literature reviews. In Handbook of eHealth Evaluation: An Evidence-based Approach. University of Victoria.
    46. Park, H. W., Park, S., & Chong, M. (2020). Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea. Journal of Medical Internet Research, 22(5), e18897. https://doi.org/10.2196/18897 Go to original source...
    47. Pempek, T. A., Yermolayeva, Y. A., & Calvert, S. L. (2009). College students' social networking experiences on Facebook. Journal of Applied Developmental Psychology, 30(3), 227-238. https://doi.org/10.1016/j.appdev.2008.12.010 Go to original source...
    48. PRISMA. (2015). PRISMA Checklist. http://prisma-statement.org/PRISMAStatement/Checklist.aspx
    49. Rizo, C. A., Lupea, D., Baybourdy, H., Anderson, M., Closson, T., & Jadad, A. R. (2005). What Internet services would patients like from hospitals during an epidemic? Lessons from the SARS outbreak in Toronto. Journal of Medical Internet Research, 7(4), e46. https://doi.org/10.2196/jmir.7.4.e46 Go to original source...
    50. Rufai, S. R., & Bunce, C. (2020). World leaders' usage of Twitter in response to the COVID-19 pandemic: a content analysis. Journal of Public Health, 42(3), 510-516. https://doi.org/10.1093/pubmed/fdaa049 Go to original source...
    51. Sarker, A., Lakamana, S., Hogg-Bremer, W., Xie, A., Al-Garadi, M. A., & Yang, Y. C. (2020). Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource. Journal of the American Medical Informatics Association, 27(8), 1310-1315. https://doi.org/10.1093/jamia/ocaa116 Go to original source...
    52. Scott, K. K., & Errett, N. A. (2018). Content, Accessibility, and Dissemination of Disaster Information via Social Media During the 2016 Louisiana Floods. Journal of Public Health Management and Practice, 24(4), 370-379. https://doi.org/10.1097/phh.0000000000000708 Go to original source...
    53. Scheufele, D. A., & Krause, N. M. (2019). Science audiences, misinformation, and fake news. Proceedings of the National Academy of Sciences, 116(16), 7662-7669. https://doi.org/10.1073/pnas.1805871115 Go to original source...
    54. Su, Y., Xue, J., Liu, X., Wu, P., Chen, J., Chen, C., … Zhu, T. (2020). Examining the Impact of COVID-19 Lockdown in Wuhan and Lombardy: A Psycholinguistic Analysis on Weibo and Twitter. International journal of environmental research and public health, 17(12), Article 4552. https://doi.org/10.3390/ijerph17124552 Go to original source...
    55. Sunstein, C. (2014). On Rumors: How Falsehoods Spread, Why We Believe Them, and What Can Be Done. Princeton University Press. Go to original source...
    56. Tang, Z., Zhang, L., Xu, F., & Vo, H. (2015). Examining the role of social media in California's drought risk management in 2014. Natural Hazards, 79(1), 171-193. https://doi.org/10.1007/s11069-015-1835-2 Go to original source...
    57. Thelwall, M., & Levitt, J. M. (2020). Retweeting Covid-19 disability issues: Risks, support and outrage. El profesional de la información, 29(2), e290216. https://doi.org/10.3145/epi.2020.mar.16 Go to original source...
    58. Tim, Y., Pan, S. L., Ractham, P., & Kaewkitipong, L. (2017). Digitally enabled disaster response: the emergence of social media as boundary objects in a flooding disaster. Information Systems Journal, 27(2), 197-232. https://doi.org/10.1111/isj.12114 Go to original source...
    59. Vieweg, S. (2010). Microblogged contributions to the emergency arena: Discovery, interpretation and implications. In Proceedings of the Computer Supported Collaborative Work 2010 (pp. 515-516). ACM.
    60. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. https://doi.org/10.1126/science.aap9559 Go to original source...
    61. Wahbeh, A., Nasralah, T., Al-Ramahi, M., & El-Gayar, O. (2020). Mining Physicians' Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis. JMIR Public Health Surveill, 6(2), e19276. https://doi.org/10.2196/19276 Go to original source...
    62. Walther, J., & Parks, M. (2002). Cues Filtered Out, Cues Filtered in: Computer Mediated Communication and Relationships. In Handbook of interpersonal communication (pp. 529-563). Sage.
    63. Wang, Y., Wang, T., Ye, X., Zhu, J., & Lee, J. (2015). Using social media for emergency response and urban sustainability: A case study of the 2012 Beijing rainstorm. Sustainability, 8(1), 1-17. https://doi.org/10.3390/su8010025 Go to original source...
    64. Wang, W., Ma, Y., Wu, T., Dai, Y., Chen, X., & Braunstein, L. A. (2019). Containing misinformation spreading in temporal social networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(12), Article 123131. https://doi.org/10.1063/1.5114853 Go to original source...
    65. Yang, X., Ye, X., & Sui, D. Z. (2016). We know where you are: In space and place-enriching the geographical context through social media. International Journal of Applied Geospatial Research, 7(2), 61-75. https://doi.org/10.4018/IJAGR.2016040105 Go to original source...
    66. Yum, S. (2020). Social Network Analysis for Coronavirus (COVID-19) in the United States. Social Science Quarterly, 101(4), 1642-1647. https://doi.org/10.1111/ssqu.12808 Go to original source...

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