Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Journal Article · Tue May 25 00:00:00 EDT 2021 · Journal of Medical Internet Research

DOI:https://doi.org/10.2196/27059· OSTI ID:1827580

Daughton, Ashlynn R. ^[1]; Shelley, Courtney D. ^[1]; Barnard, Martha ^[1]; Gerts, Dax ^[1]; Watson Ross, Chrysm ^[2]; Crooker, Isabel ^[1]; Nadiga, Gopal ^[1]; Mukundan, Nilesh ^[1]; Vaquera Chavez, Nidia Yadira ^[2]; Parikh, Nidhi ^[1]; Pitts, Travis ^[1]; Fairchild, Geoffrey ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of New Mexico, Albuquerque, NM (United States)

Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1827580
• Report Number(s): LA-UR--21-20074
• Journal Information: Journal of Medical Internet Research, Journal Name: Journal of Medical Internet Research Journal Issue: 5 Vol. 23; ISSN 1438-8871
• Publisher: JMIR Publications
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES
COVID-19
COVID-19 intervention
Twitter
coronavirus
human behavior
infectious disease
infodemiology
information science
infoveillance
mobility
shelter-in-place
social distancing
social media