Open Access Open Access  Restricted Access Subscription or Fee Access

Data-driven inferences of agency-level risk and response communication on COVID-19 through social media-based interactions

Md Ashraf Ahmed, PhD Candidate, Arif Mohaimin Sadri, PhD, M. Hadi Amini, PhD, DEng

Abstract


Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response—ie, timing of lockdown, timing of reopening, etc.—and disease outbreak indicators—ie, number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics—eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19.


Keywords


COVID-19, Twitter, social media, public organizations, interactions, historical data, policy

Full Text:

PDF

References


Terhakopian A, Benedek DM: Hospital disaster preparedness: Mental and behavioral health interventions for infectious disease outbreaks and bioterrorism incidents. Am J Disaster Med. 2007; 2(1): 43-50.

Mortula MM, Ahmed MA, Sadri AM, et al.: Improving resiliency of water supply system in arid regions: Integrating centrality and hydraulic vulnerability. J Manage Eng. 2020; 36: 05020011.

Ahmed MA, Sadri AM, Hadi M: The role of social networks and day-to-day sharing activity on hurricane evacuation decision consistency and shared evacuation capacity. In Proceedings of the 99th Annual Meeting of Transportation Research Board, Washington, DC, USA, 2020.

NRC: National Research Council report, disaster resilience: A national imperative, 2012.

Chong KC, Cheng W, Zhao S, et al.: Monitoring disease transmissibility of 2019 novel coronavirus disease in Zhejiang, China. medRxiv. 2020.

Zhang Y, Jiang B, Yuan J, et al.: The impact of social distancing and epicenter lockdown on the COVID-19 epidemic in mainland China: A data-driven SEIQR model study. medRxiv. 2020.

Wilder-Smith A, Freedman D: Isolation, quarantine, social distancing and community containment: Pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. J Travel Med. 2020; 27.

Dalton C, Corbett S, Katelaris A: Pre-emptive low cost social distancing and enhanced hygiene implemented before local COVID-19 transmission could decrease the number and severity of cases. Available at SSRN 3549276. 2020.

Ahmed MA, Sadri AM, Pradhananga P, et al.: Social media communication patterns of construction industry in major disasters. In Construction Research Congress Proceedings-2020. 2020.

Roy KC, Ahmed MA, Hasan S, et al.: Dynamics of crisis communications in social media: Spatio-temporal and text-based comparative analyses of twitter data from Hurricanes Irma and Michael. In Proceedings of the International Conference on Information Systems for Crisis Response and Management (ISCRAM) 2020. 2020.

Mojumder MN, Ahmed MA, Sadri AM: Identifying ridesharing risk, response, and challenges in the emergence of novel coronavirus using interactions in uber drivers forum. Front Built Environ. 2021; 7.

Brown J: Is social media the key to effective communication during campus emergencies. Govtech. 2015. Available at http://www.govtech.com/education/Is-Social-Media-the-Key-to-Effective-Communication-During-Campus-Emergencies.html.

Sadri AM, Ukkusuri SV, Ahmed MA: Review of social influence in crisis communications and evacuation decision-making. Transp Res Interdiscip Perspect. 2021; 9: 100325.

Sadri AM, Hasan S, Ukkusuri SV, et al.: Understanding information spreading in social media during Hurricane Sandy: User activity and network properties. arXiv preprint arXiv:170603019.2017.

Morshed SA, Arafat M, Ahmed MA, et al. Discovering the commuters’ assessments on disaster resilience of transportation infrastructure. In Proceedings of ASCE International Conference of Transportation and Development (ICTD), 2020. 2020.

Zhang C, Fan C, Yao W, et al.: Social media for intelligent public information and warning in disasters: An interdisciplinary review. Int J Inform Manag. 2019; 49: 190-207.

Austin L, Fisher Liu B, Jin Y: How audiences seek out crisis information: Exploring the social-mediated crisis communication model. J Appl Commun Res. 2012; 40(2): 188-207.

Freimuth VS, Hilyard KM, Barge JK, et al.: Action, not talk: A simulation of risk communication during the first hours of a pandemic. Health Promotion Practice. 2008; 9(4_suppl): 35S-44S.

Palen L, Hughes AL: Social media in disaster communication. In Rodriguez H, Donner W, Trainor JE (eds.): Handbook of Disaster Research. Berlin: Springer International Publishing, 2018: 497-518.

Monahan B, Ettinger M: News media and disasters: Navigating old challenges and new opportunities in the digital age. In Rodriguez H, Donner W, Trainor JE (eds.): Handbook of Disaster Research. Berlin: Springer International Publishing, 2018: 479-495.

Battur R, Yaligar N: Twitter bot detection using machine learning algorithms. Int J Sci Res. 2020; 8: 304-307.

Asr FT, Taboada M: The data challenge in misinformation detection: Source reputation vs. content veracity. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER). Association for Computational Linguistics, 2018: 10-15.

Huang S-K, Lindell MK, Prater CS: Who leaves and who stays? A review and statistical meta-analysis of hurricane evacuation studies. Environ Behav. 2016; 48(8): 991-1029.

Agarwal A, Xie B, Vovsha I, et al.: Sentiment analysis of twitter data. In Proceedings of the Workshop on Language in Social Media (LSM 2011). 2011: 30-38.

Wang X, Wei F, Liu X, et al.: Topic sentiment analysis in twitter: A graph-based hashtag sentiment classification approach. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM. 2011: 1031-1040.

Hutto C, Gilbert E: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 2014.

Beri A: Sentimental analysis using VADER—Interpretation and classification of emotions. Available at https://towardsdatascience.com/sentimental-analysis-using-vader-a3415fef7664#:~:text=VADER%20(%20Valence%20Aware%20Dictionary%20for,intensity%20(strength)%20of%20emotion.&text=The%20sentiment%20score%20of%20a,each%20word%20in%20the%20text.

Blei D: Probabilistic topic models. Commun ACM. 2012; 55(4): 77-84. DOI: 10.1145/2133806.213382.

Blei DM, Lafferty JD: Dynamic topic models. In ICML’06: Proceedings of the 23rd International Conference on Machine Learning. 2006; 113-120.

Dyer H: The story of worldometer, the quick project that became one of the most popular sites on the internet. Available at https://www.newstatesman.com/science-tech/coronavirus/2020/05/story-worldometer-quick-project-became-one-most-popular-sites.

Worldometer: COVID-19 coronavirus pandemic data. Available at https://www.worldometers.info/coronavirus/country/us/.

Barua Z, Barua S, Aktar S, et al.: Effects of misinformation on COVID-19 individual responses and recommendations for resilience of disastrous consequences of misinformation. Prog Disaster Sci. 2020; 8: 100119.

Safavian SR, Landgrebe D: A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern. 1991; 21(3): 660-674.

Anthony M, Bartlett PL: Neural Network Learning: Theoretical Foundations. Cambridge: Cambridge University Press, 2009.

Breiman L: Random forests. Mach Learn. 2001; 45(1): 5-32.

Chou W-YS, Oh A, Klein WM: Addressing health-related misinformation on social media. JAMA. 2018; 320(23): 2417-2418.

Wu L, Morstatter F, Carley KM, et al.: Misinformation in social media: Definition, manipulation, and detection. SIGKDD Explor Newsl. 2019; 21(2): 80-90.

Sadri AM, Hasan S, Ukkusuri SV: Joint inference of user community and interest patterns in social interaction networks. Soc Netw Anal Min. 2019; 9(1): 11.

Sadri AM, Hasan S, Ukkusuri SV, et al.: Exploring network properties of social media interactions and activities during hurricane sandy. Transp Res Interdiscip Perspect. 2020;6:100-143.




DOI: https://doi.org/10.5055/jem.0589

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Journal of Emergency Management