Digital Mass Hysteria during Pandemic? A Study of Twitter Communication Patterns in the US during the Stages of COVID-19 Vaccination
Abstract
:1. Introduction
2. Theoretical Background
2.1. US COVID-19 Vaccination Management Plan
2.2. Mass Hysteria and Sensemaking in Crises
2.3. Theoretical Framework
3. Materials and Methods
4. Results
4.1. Descriptive Statistics
4.2. Interrupted Time Series Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Britton, A. Effectiveness of the Pfizer-BioNTech COVID-19 vaccine among residents of two skilled nursing facilities experiencing COVID-19 outbreaks—Connecticut, December 2020–February 2021. Morb. Mortal. Wkly. Rep. 2021, 70, 396. [Google Scholar] [CrossRef] [PubMed]
- Painter, E.M. Demographic characteristics of persons vaccinated during the first month of the COVID-19 vaccination program—United States, December 14, 2020–January 14, 2021. Morb. Mortal. Wkly. Rep. 2021, 70, 174. [Google Scholar] [CrossRef] [PubMed]
- Woko, C.; Siegel, L.; Hornik, R. An investigation of low COVID-19 vaccination intentions among Black Americans: The role of behavioral beliefs and trust in COVID-19 information sources. J. Health Commun. 2020, 25, 819–826. [Google Scholar] [CrossRef] [PubMed]
- Dhawan, D.; Bekalu, M.; Pinnamaneni, R.; McCloud, R.; Viswanath, K. COVID-19 News and Misinformation: Do They Matter for Public Health Prevention? J. Health Commun. 2021, 26, 799–808. [Google Scholar] [CrossRef] [PubMed]
- Gyftopoulos, S.; Drosatos, G.; Fico, G.; Pecchia, L.; Kaldoudi, E. Analysis of Pharmaceutical Companies’ Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public. Behav. Sci. 2024, 14, 128. [Google Scholar] [CrossRef] [PubMed]
- Tillman, G.; March, E.; Lavender, A.P.; Braund, T.A.; Mesagno, C. Disordered social media use during COVID-19 predicts perceived stress and depression through indirect effects via fear of COVID-19. Behav. Sci. 2023, 13, 698. [Google Scholar] [CrossRef] [PubMed]
- Clements, C.J. Mass psychogenic illness after vaccination. Drug Saf. 2003, 26, 599–604. [Google Scholar] [CrossRef] [PubMed]
- CDC. Vaccination Program Interim Playbook for Jurisdiction Operations; CDC: Atlanta, GA, USA, 2020. [Google Scholar]
- Gardikiotis, A.; Malinaki, E.; Charisiadis-Tsitlakidis, C.; Protonotariou, A.; Archontis, S.; Lampropoulou, A.; Maraki, I.; Papatheodorou, K.; Zafeiriou, G. Emotional and cognitive responses to COVID-19 information overload under lockdown predict media attention and risk perceptions of COVID-19. J. Health Commun. 2021, 26, 434–442. [Google Scholar] [CrossRef]
- Dry, S.; Leach, M. Epidemics: Science, Governance and Social Justice; Routledge: London, UK, 2010. [Google Scholar]
- Snowden, F.M. Epidemics and Society: From the Black Death to the Present; Yale University Press: New Haven, CO, USA, 2019. [Google Scholar]
- Taha, S.; Matheson, K.; Cronin, T.; Anisman, H. Intolerance of uncertainty, appraisals, coping, and anxiety: The case of the 2009 H 1 N 1 pandemic. Br. J. Health Psychol. 2014, 19, 592–605. [Google Scholar] [CrossRef]
- Goldstone, B.J.; Brown, B. The role of public knowledge, resources, and innovation in responding to the Ebola outbreak. Disaster Med. Public Health Prep. 2015, 9, 595–597. [Google Scholar] [CrossRef]
- Nagpal, S.J.S.; Karimianpour, A.; Mukhija, D.; Mohan, D.; Brateanu, A. YouTube videos as a source of medical information during the Ebola hemorrhagic fever epidemic. SpringerPlus 2015, 4, 457. [Google Scholar] [CrossRef] [PubMed]
- Alexander, K.A.; Sanderson, C.E.; Marathe, M.; Lewis, B.L.; Rivers, C.M.; Shaman, J.; Drake, J.M.; Lofgren, E.; Dato, V.M.; Eisenberg, M.C.; et al. What factors might have led to the emergence of Ebola in West Africa? PLoS Neglected Trop. Dis. 2015, 9, e0003652. [Google Scholar] [CrossRef] [PubMed]
- Hanson, J.; Faley, P.S.; Quinn, M. Analysis of the Liberian Ebola Survivors Support System (ESSS). Integr. J. Glob. Health 2017, 1, 2. [Google Scholar]
- Saurabh, S.; Prateek, S. Role of contact tracing in containing the 2014 Ebola outbreak: A review. Afr. Health Sci. 2017, 17, 225–236. [Google Scholar] [CrossRef]
- Hanson-DeFusco, J.; Shi, M.; Du, Z.; Zounon, O.; Hounnouvi, F.M.; DeFusco, A. Systems analysis of the effects of the 2014-16 Ebola crisis on WHO-reporting nations’ policy adaptations and 2020-21 COVID-19 response: A systematized review. Glob. Health 2023, 19, 96. [Google Scholar] [CrossRef] [PubMed]
- Aïmeur, E.; Amri, S.; Brassard, G. Fake news, disinformation and misinformation in social media: A review. Soc. Netw. Anal. Min. 2023, 13, 30. [Google Scholar] [CrossRef] [PubMed]
- Pérez-Escolar, M.; Lilleker, D.; Tapia-Frade, A. A systematic literature review of the phenomenon of disinformation and misinformation. Media Commun. 2023, 11, 76–87. [Google Scholar] [CrossRef]
- Baines, D.; Elliott, R.J. Defining misinformation, disinformation and malinformation: An urgent need for clarity during the COVID-19 infodemic. Discuss. Pap. 2020, 20, 20-06. [Google Scholar]
- Zhao, G.; Cheng, Q.; Dong, X.; Xie, L. Mass hysteria attack rates in children and adolescents: A meta-analysis. J. Int. Med. Res. 2021, 49, 03000605211039812. [Google Scholar] [CrossRef]
- Weir, E. Mass sociogenic illness. Can. Med. Assoc. J. 2005, 172, 36. [Google Scholar] [CrossRef]
- Dubey, S.; Biswas, P.; Ghosh, R.; Chatterjee, S.; Dubey, M.J.; Chatterjee, S.; Lahiri, D.; Lavie, C. J. Psychosocial impact of COVID-19. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 779–788. [Google Scholar] [CrossRef]
- Ancona, D. Leadership in an Age of Uncertainty. Cent. Bus. Res. Brief 2005, 6, 1–3. [Google Scholar]
- Aron, D.C.; Leykum, L. Sensemaking: Appreciating Patterns and Coherence in Complexity, in Implementation Science; Routledge: London, UK, 2022; p. 9698. [Google Scholar]
- Weick, K.E. Enacted sensemaking in crisis situations. J. Manag. Stud. 1988, 25, 305–317. [Google Scholar] [CrossRef]
- Weik, K.E. The collapse of sensemaking in organizations: The Mann Gulch disaster. Administrative Science Quarterly 1993, 38, 628–652. [Google Scholar] [CrossRef]
- Fligstein, N.; McAdam, D. A Theory of Fields; Oxford University Press: Oxford, UK, 2012. [Google Scholar]
- Linden, A. Conducting interrupted time-series analysis for single-and multiple-group comparisons. Stata J. 2015, 15, 480–500. [Google Scholar] [CrossRef]
- Al-Garadi, M.A.; Khan, M.S.; Varathan, K.D.; Mujtaba, G.; Al-Kabsi, A.M. Using online social networks to track a pandemic: A systematic review. J. Biomed. Informatics 2016, 62, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Xu, M.; Luo, Z.; Xu, H.; Wang, B. Media bias and factors affecting the impartiality of news agencies during COVID-19. Behav. Sci. 2022, 12, 313. [Google Scholar] [CrossRef] [PubMed]
- Allcott, H.; Boxell, L.; Conway, J.; Gentzkow, M.; Thaler, M.; Yang, D. Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic. J. Public Econ. 2020, 191, 104254. [Google Scholar] [CrossRef] [PubMed]
- Bergengruen, V. How the anti-vax movement is taking over the right. Time 2022, 7, 2022. [Google Scholar]
- Nerini, A.; Duradoni, M.; Matera, C.; Guazzini, A.; Paradisi, M.; Schembri, A. Predictors of Vaccination Intentions and Behaviour during the COVID-19 pandemic in Italy. Behav. Sci. 2023, 13, 950. [Google Scholar] [CrossRef]
- Bolsen, T.; Palm, R. Politicization and COVID-19 vaccine resistance in the US. Prog. Mol. Biol. Transl. Sci. 2022, 188, 81. [Google Scholar]
- Hart, P.S.; Chinn, S.; Soroka, S. Politicization and polarization in COVID-19 news coverage. Sci. Commun. 2020, 42, 679–697. [Google Scholar] [CrossRef] [PubMed]
- Stroebe, W.; Vandellen, M.R.; Abakoumkin, G.; Lemay, E.P.; Schiavone, W.M.; Agostini, M.; Bélanger, J.J.; Gützkow, B.; Kreienkamp, J.; Reitsema, A.M.; et al. Politicization of COVID-19 health-protective behaviors in the United States: Longitudinal and cross-national evidence. PLoS ONE 2021, 16, e0256740. [Google Scholar] [CrossRef] [PubMed]
- Lang, J.; Erickson, W.W.; Jing-Schmidt, Z. # MaskOn!# MaskOff! Digital polarization of mask-wearing in the United States during COVID-19. PLoS ONE 2021, 16, e0250817. [Google Scholar]
- Urman, A.; Ionescu, S.; Garcia, D.; Hannák, A. The politicization of medical preprints on Twitter during the early stages of COVID-19 pandemic. J. Quant. Descr. Digit. Media 2022, 2, 1–46. [Google Scholar] [CrossRef]
- D’Andrea, E.; Ducange, P.; Bechini, A.; Renda, A.; Marcelloni, F. Monitoring the public opinion about the vaccination topic from tweets analysis. Expert Syst. Appl. 2019, 116, 209–226. [Google Scholar] [CrossRef]
- Mittelmeier, J.; Cockayne, H. Global representations of international students in a time of crisis: A qualitative analysis of Twitter data during COVID-19. Int. Stud. Sociol. Educ. 2023, 32, 487–510. [Google Scholar] [CrossRef]
Tweets before Preprocessing | After Preprocessing | Score |
---|---|---|
There was a lot of hope and optimism in the library-turned-vaccination-suite. We were required to stay 15–30 min after the shot for monitoring and it was nice to just talk and laugh with others to feel like we were sharing a small victory. It’s been in short supply. | lot hope optim library turned vaccination suit requir stay 1530 minut shot monitor nice just talk laugh other feel like share small victori short suppli | |9| |
RT @pettapallath @POTUS @VP Thanks for taking steps to make vaccination available for everyone before the target date. It would be great i?? | thank take step make vaccin avail everyon target date great | |5| |
RT @WhiteHouse\I will get vaccinated as soon as possible and I urge you to do the same. We need to protect our vulnerable neighbors rebu… | get vaccin soon possibl urg need protect vulner neighbor rebu | |1| |
2 vaccination sites shut down in #ScarbTO with approximately 10,000 appointments cancelled. Where? the outrage by our #onpoli #TOpoli elected officials for our communities? Playing politics with our lives & staying silent while health inequities continue to grow?! We see you. | 2 vaccin site shut approxim 10,000 appoint cancel outrag elect offici communiti play polit live stay silent health inequ continu grow see | |−1| |
@BjStov Me neither not that I think there is anything wrong with it. I just don’t know what the long term ramifications of the vaccine. Every year the flu vac is usually wrong. imho its not been tested enough. | neither think anyth wrong just know long term ramif vaccin everi year flu vac usual wrong imho test enough | |−6| |
@Nation985 @Mr_Grant_I @notagain_ohno @NBCNews In the real world people will become ill and die from the vaccine. That’s a fact. WTF is wrong with you people? | real world peopl becom ill die vaccin fact wtf wrong people | |−11| |
Average Sentiments | Overall Sentiment Average | Negative Sentiment Average | Positive Sentiment Average | |||
---|---|---|---|---|---|---|
Coef. | t | Coef. | t | Coef. | t | |
Phase 1 | 0.089 (0.018) | 4.74 *** | 0.073 (0.019) | 3.72 *** | 0.012 (0.017) | 0.72 |
Phase 2 start effect | −0.163 (0.249) | −0.66 | −0.552 (0.244) | −2.26 ** | −0.265 (0.168) | −1.58 |
Phase 2 over time | −0.104 (0.020) | −5.12 *** | −0.063 (0.022) | −2.87 *** | −0.008 (0.017) | −0.47 |
Phase 3 start effect | 0.179 (0.148) | 1.20 | −0.150 (0.142) | −1.06 | 0.0427 (0.133) | 0.32 |
Phase 3 over time | 0.016 (0.015) | 1.03 | 0.029 (0.016) | 1.78 * | −0.014 (0.021) | −0.71 |
intercept | −1.283 (0.097) | −13.10 | 3.625 (0.185) | 19.54 | 2.257 (0.138) | 16.31 |
Observation | 52 | 52 | 52 | |||
F (5, 46) | 18.81 (Prob > F = 0.000) | 5.57 (Prob > F = 0.000) | 0.77 (Prob > F = 0.574) | |||
R2 | 0.5285 | 0.3444 | 0.0802 | |||
rho | 0.0210 | 0.0083 | −0.0323 | |||
Durbin-Watson | 1.9865 | 1.9652 | 1.9210 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jeong, D.; Hanson-DeFusco, J.; Kim, D.; Lee, C.-K. Digital Mass Hysteria during Pandemic? A Study of Twitter Communication Patterns in the US during the Stages of COVID-19 Vaccination. Behav. Sci. 2024, 14, 389. https://doi.org/10.3390/bs14050389
Jeong D, Hanson-DeFusco J, Kim D, Lee C-K. Digital Mass Hysteria during Pandemic? A Study of Twitter Communication Patterns in the US during the Stages of COVID-19 Vaccination. Behavioral Sciences. 2024; 14(5):389. https://doi.org/10.3390/bs14050389
Chicago/Turabian StyleJeong, Dohyo, Jessi Hanson-DeFusco, Dohyeong Kim, and Chang-Kil Lee. 2024. "Digital Mass Hysteria during Pandemic? A Study of Twitter Communication Patterns in the US during the Stages of COVID-19 Vaccination" Behavioral Sciences 14, no. 5: 389. https://doi.org/10.3390/bs14050389
APA StyleJeong, D., Hanson-DeFusco, J., Kim, D., & Lee, C. -K. (2024). Digital Mass Hysteria during Pandemic? A Study of Twitter Communication Patterns in the US during the Stages of COVID-19 Vaccination. Behavioral Sciences, 14(5), 389. https://doi.org/10.3390/bs14050389