Back
 AJC  Vol.10 No.2 , June 2022
How People Show Their Emotions towards COVID-19 on Twitter Platform
Abstract: The pandemic that has lasted for more than three years exerted a significant influence on the life of people, especially on their physical and mental health of people. The study intends to find out how people express their sentiments on social media platformsTwitter. It will adopt the quantitative methodology in analyzing the data. The secondary data will be collected from the Web page via the Chrome extension with WDRA software. By means of making the analysis, the study shows that there was a great change before and after the pandemic.
Cite this paper: Zhao, L. (2022) How People Show Their Emotions towards COVID-19 on Twitter Platform. Advances in Journalism and Communication, 10, 170-198. doi: 10.4236/ajc.2022.102012.
References

[1]   Aarons, G. A., Hurlburt, M., & Horwitz, S. M. (2011). Advancing a Conceptual Model of Evidence-Based Practice Implementation in Public Service Sectors. Administration and Policy in Mental Health and Mental Health Services Research, 38, 4-23.
https://doi.org/10.1007/s10488-010-0327-7

[2]   Ahmad, N., & Siddique, J. (2017). Personality Assessment Using Twitter Tweets. Procedia Computer Science, 112, 1964-1973.
https://doi.org/10.1016/j.procs.2017.08.067

[3]   Al-Dmour, H., Salman, A., Abuhashesh, M., & Al-Dmour, R. (2020). Influence of Social Media Platforms on Public Health Protection against the COVID-19 Pandemic via the Mediating Effects of Public Health Awareness and Behavioral Changes: Integrated Model. Journal of Medical Internet Research, 22, Article ID: e19996.
https://doi.org/10.2196/19996

[4]   Allgaier, J., & Svalastog, A. L. (2015). The Communication Aspects of the Ebola Virus Disease Outbreak in Western Africa—Do We Need to Counter One, Two, or Many Epidemics? Croatian Medical Journal, 56, 496-499.
https://doi.org/10.3325/cmj.2015.56.496

[5]   Amirthalingam, G., Whitaker, H., Brooks, T., Brown, K., Hoschler, K., Linley, E. et al. (2021). Seroprevalence of SARS-CoV-2 among Blood Donors and Changes after Introduction of Public Health and Social Measures, London, UK. Emerging Infectious Diseases, 27, 1795-1801.
https://doi.org/10.3201/eid2707.203167

[6]   Ampofo, L., Collister, S., O’Loughlin, B., Chadwick, A., Halfpenny, P. J., & Procter, P. J. (2015). Text Mining and Social Media: When Quantitative Meet Qualitative and Software Meet People. In P. Halfpenny & R. Procter (Eds.), Innovations in Digital Research Methods (pp. 161-192). Sage.
https://doi.org/10.4135/9781473920651.n8

[7]   Ayouni, I., Maatoug, J., Dhouib, W., Zammit, N., Fredj, S. B., Ghammam, R., & Ghannem, H. (2021). Effective Public Health Measures to Mitigate the Spread of COVID-19: A Systematic Review. BMC Public Health, 21, Article ID: 1015.
https://doi.org/10.1186/s12889-021-11111-1

[8]   Bennett, G. G., & Glasgow, R. E. (2009). The Delivery of Public Health Interventions via the Internet: Actualizing Their Potential. Annual Review of Public Health, 30, 273-292.
https://doi.org/10.1146/annurev.publhealth.031308.100235

[9]   Boon-Itt, S., & Skunkan, Y. (2020). Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health and Surveillance, 6, Article ID: e21978.
https://doi.org/10.2196/21978

[10]   Chandrasekaran, R., Mehta, V., Valkunde, T., & Moustakas, E. (2020). Twitter Talks on COVID-19: A Temporal Examination of Topics, Trends, and Sentiments. Journal of Medical Internet Research, 22, Article ID: e22624.
https://doi.org/10.2196/22624

[11]   Chun, S. A., Li, A. C. Y., Toliyat, A., & Geller, J. (2020). Tracking Citizen’s Concerns during the COVID-19 Pandemic. In S.-J. Eom, & J. Lee (Eds.), The 21st Annual International Conference on Digital Government Research (pp. 322-323). Association for Computing Machinery.
https://doi.org/10.1145/3396956.3397000

[12]   Daoust, J. F., Nadeau, R., Dassonneville, R., Lachapelle, E., Bélanger, é., Savoie, J., & van der Linden, C. (2021). How to Survey Citizens’ Compliance with COVID-19 Public Health Measures: Evidence from Three Survey Experiments. Journal of Experimental Political Science, 8, 310-317.
https://doi.org/10.1017/XPS.2020.25

[13]   Dickerson, M. (2018). A Gentle Introduction to Text Analysis with Voyant Tools. eScholarship, University of California.

[14]   Du, J., Xu, J., Song, H., Liu, X., & Tao, C. (2017). Optimization on Machine Learning Based Approaches for Sentiment Analysis on HPV Vaccines Related Tweets. Journal of Biomedical Semantics, 8, Article No. 9.
https://doi.org/10.1186/s13326-017-0120-6

[15]   Dunn, A. G., Mandl, K. D., & Coiera, E. (2018). Social Media Interventions for Precision Public Health: Promises and Risks. NPJ Digital Medicine, 1, Article No. 47.
https://doi.org/10.1038/s41746-018-0054-0

[16]   Figueira, O., Hatori, Y., Liang, L., Chye, C., & Liu, Y. (2021). Understanding COVID-19 Public Sentiment towards Public Health Policies Using Social Media Data. In 2021 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 8-15). Institute of Electrical and Electronics Engineers.
https://doi.org/10.1109/GHTC53159.2021.9612509

[17]   Giustini, D., Ali, S. M., Fraser, M., & Boulos, M. N. K. (2018). Effective Uses of Social Media in Public Health and Medicine: A Systematic Review of Systematic Reviews. Online Journal of Public Health Informatics, 10.
https://doi.org/10.5210/ojphi.v10i2.8270

[18]   Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, K., Martinez-Hernandez, V., Sanchez, V., & Perez-Meana, H. (2018). A Web Scraping Methodology for Bypassing Twitter API Restrictions. arXiv preprint arXiv:1803.09875.

[19]   Himelboim, I., Xiao, X., Lee, D. K. L., Wang, M. Y., & Borah, P. (2020). A Social Networks Approach to Understanding Vaccine Conversations on Twitter: Network Clusters, Sentiment, and Certainty in HPV Social Networks. Health Communication, 35, 607-615.
https://doi.org/10.1080/10410236.2019.1573446

[20]   HT Tech (2012, September). Twitter Guide: Express Yourself in 140 Characters.
https://tech.hindustantimes.com/tech/news/twitter-guide-express-yourself-in-140-characters-story-Ylqe0cYn6mZzrP4IJvbsuN.html

[21]   Ji, X., Chun, S., Wei, Z., & Geller, J. (2015). Twitter Sentiment Classification for Measuring Public Health Concerns. Social Network Analysis and Mining, 5, Article No. 13.
https://doi.org/10.1007/s13278-015-0253-5

[22]   Kouzy, R., Abi Jaoude, J., Kraitem, A., El Alam, M. B., Karam, B., Adib, E. et al. (2020). Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter. Cureus, 12, Article ID: e7255.
https://doi.org/10.7759/cureus.7255

[23]   Le, N. K., Le, A. V., Brooks, J. P., Khetpal, S., Liauw, D., Izurieta, R., & Reina Ortiz, M. (2020). Impact of Government-Imposed Social Distancing Measures on COVID-19 Morbidity and Mortality around the World. Bulletin of the World Health Organization, 10, 1-20.

[24]   Lin, Y., Hu, Z., Alias, H., & Wong, L. P. (2020). Influence of Mass and Social Media on Psychobehavioral Responses among Medical Students during the Downward Trend of COVID-19 in Fujian, China: Cross-Sectional Study. Journal of Medical Internet Research, 22, Article ID: e19982.
https://doi.org/10.2196/19982

[25]   Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5, 1-167.
https://doi.org/10.2200/S00416ED1V01Y201204HLT016

[26]   Liu, Q., Zheng, Z., Zheng, J., Chen, Q., Liu, G., Chen, S. et al. (2020). Health Communication through News Media during the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach. Journal of Medical Internet Research, 22, Article ID: e19118.
https://doi.org/10.2196/19118

[27]   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 and Surveillance, 6, Article ID: e19447.
https://doi.org/10.2196/19447

[28]   Mackey, T., Purushothaman, V., Li, J., Shah, N., Nali, M., Bardier, C. et al. (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 and Surveillance, 6, Article ID: e19509.
https://doi.org/10.2196/19509

[29]   Maqbool, A., & Khan, N. Z. (2020). Analyzing Barriers for Implementation of Public Health and Social Measures to Prevent the Transmission of COVID-19 Disease using DEMATEL Method. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14, 887-892.
https://doi.org/10.1016/j.dsx.2020.06.024

[30]   Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013). A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication. Journal of Medical Internet Research, 15, Article No. e85.
https://doi.org/10.2196/jmir.1933

[31]   Moran, M. (2022, January 20). 20 Top Twitter Statistics: Usage, Demographics, Trends.
https://startupbonsai.com/twitter-statistics/

[32]   Padidar, S., Liao, S. M., Magagula, S., Mahlaba, T. A. A., Nhlabatsi, N. M., & Lukas, S. (2021). Assessment of Early COVID-19 Compliance to and Challenges with Public Health and Social Prevention Measures in the Kingdom of Eswatini, Using an Online Survey. PLoS ONE, 16, Article ID: e0253954.
https://doi.org/10.1371/journal.pone.0253954

[33]   Park, A., Conway, M., & Chen, A. T. (2018). Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach. Computers in Human Behavior, 78, 98-112.
https://doi.org/10.1016/j.chb.2017.09.001

[34]   Pavlatos, C., & Vita, V. (2016). Linguistic Representation of Power System Signals. In P. Karampelas, & L. Ekonomou (Eds.), Electricity Distribution (pp. 285-295). Springer.
https://doi.org/10.1007/978-3-662-49434-9_12

[35]   Porumbescu, G. A. (2016). Linking Public Sector Social Media and E-Government Website Used to Trust in Government. Government Information Quarterly, 33, 291-304.
https://doi.org/10.1016/j.giq.2016.04.006

[36]   Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Flammini, A., & Menczer, F. (2011). Detecting and Tracking Political Abuse in Social Media. In Proceedings of the International AAAI Conference on Web and social media (Vol. 5, pp. 297-304).

[37]   Sampsel, L. J. (2018). Voyant Tools. Music Reference Services Quarterly, 21, 153-157.
https://doi.org/10.1080/10588167.2018.1496754

[38]   Saunders, G. H., Christensen, J. H., Gutenberg, J., Pontoppidan, N. H., Smith, A., Spanoudakis, G., & Bamiou, D. E. (2020). Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing. Ear and Hearing, 41, 1057-1063.
https://doi.org/10.1097/AUD.0000000000000850

[39]   Seeman, N., & Rizo, C. (2010). Assessing and Responding in Real Time to Online Anti-Vaccine Sentiment during a Flu Pandemic. Healthcare Quarterly, 13, 8-15.

[40]   Semenza, J. C., Adlhoch, C., Baka, A., Broberg, E., Cenciarelli, O., De Angelis, S. et al. (2021). COVID-19 Research Priorities for Non-Pharmaceutical Public Health and Social Measures. Epidemiology & Infection, 149, Article No. e87.
https://doi.org/10.1017/S0950268821000716

[41]   Sharevski, F. (2022). (Mis)perceptions and Engagement on Twitter: COVID-19 Vaccine Rumors on Efficacy and Mass Immunization Effort. International Journal of Information Management Data Insights, 2, Article ID: 100059.
https://doi.org/10.1016/j.jjimei.2022.100059

[42]   Shen, C. W., Chen, M., & Wang, C. C. (2019). Analyzing the Trend of O2O Commerce by Bilingual Text Mining on Social Media. Computers in Human Behavior, 101, 474-483.
https://doi.org/10.1016/j.chb.2018.09.031

[43]   Shi, W., Liu, D., Yang, J., Zhang, J., Wen, S., & Su, J. (2020). Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the Covid-19 Pandemic Discussions on Twitter. International Journal of Environmental Research and Public Health, 17, Article No. 8701.
https://doi.org/10.3390/ijerph17228701

[44]   Spangler, K. J., & Caldwell, L. L. (2007). The Implications of Public Policy Related to Parks, Recreation, and Public Health: A Focus on Physical Activity. Journal of Physical Activity and Health, 4, S64-S71.
https://doi.org/10.1123/jpah.4.s1.s64

[45]   Talic, S., Shah, S., Wild, H., Gasevic, D., Maharaj, A., Ademi, Z. et al. (2021). Effectiveness of Public Health Measures in Reducing the Incidence of Covid-19, SARS-CoV-2 Transmission, and Covid-19 Mortality: Systematic Review and Meta-Analysis. BMJ, 375, Article ID: e068302.

[46]   Tan, J. Y., Conceicao, E. P., Sim, X. Y. J., Wee, L. E. I., Aung, M. K., & Venkatachalam, I. (2020). Public Health Measures during COVID-19 Pandemic Reduced Hospital Admissions for Community Respiratory Viral Infections. Journal of Hospital Infection, 106, 387-389.
https://doi.org/10.1016/j.jhin.2020.07.023

[47]   Thelwall, M. (2017). The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength. In J. Holyst (Ed.), Cyberemotions (pp. 119-134). Springer.
https://doi.org/10.1007/978-3-319-43639-5_7

[48]   Thelwall, M. (2018). Gender Bias in Sentiment Analysis. Online Information Review, 42, 45-57.
https://doi.org/10.1108/OIR-05-2017-0139

[49]   Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter Events. Journal of the American Society for Information Science and Technology, 62, 406-418.
https://doi.org/10.1002/asi.21462

[50]   Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment Strength Detection for the Social Web. Journal of the American Society for Information Science and Technology, 63, 163-173.
https://doi.org/10.1002/asi.21662

[51]   Thelwall, M., Buckley, K., Paltoglou, G., Skowron, M., Garcia, D., Gobron, S. et al. (2013). Damping Sentiment Analysis in Online Communication: Discussions, Monologs and Dialogs. In A. Gelbukh (Ed.), International Conference on Intelligent Text Processing and Computational Linguistics (pp. 1-12). Springer.
https://doi.org/10.1007/978-3-642-37256-8_1

[52]   Vanagas, G., Bala, M., & Lhachimi, S. K. (2017). Evidence-Based Public Health 2017. BioMed Research International, 2017, Article ID: 2607397.
https://doi.org/10.1155/2017/2607397

[53]   World Health Organization (WHO) (2020a). Considerations for School-Related Public Health Measures in the Context of COVID-19: Annex to Considerations in Adjusting Public Health and Social Measures in the Context of COVID-19, 14 September 2020. No. WHO/2019-nCoV/Adjusting_PH_measures/Schools/2020.2, World Health Organization.

[54]   World Health Organization (WHO) (2020b). Overview of Public Health and Social Measures in the Context of COVID-19: Interim Guidance, 18 May 2020. No. WHO/2019-nCoV/PHSM_Overview/2020.1, World Health Organization.

[55]   World Health Organization (WHO) (2020c, March 16). WHO Director-General’s Opening Remarks at the Media Briefing on Covid-19—16 March 2020.
https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---16-march-2020

[56]   Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020). Twitter Discussions and Emotions about the COVID-19 Pandemic: Machine Learning Approach. Journal of Medical Internet Research, 22, Article ID: e20550.
https://doi.org/10.2196/20550

[57]   Zadeh, A. H., Zolbanin, H. M., Sharda, R., & Delen, D. (2019). Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis. Information Systems Frontiers, 21, 743-760.
https://doi.org/10.1007/s10796-018-9893-0

 
 
Top