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.
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Identifying Ridesharing Risk, Response, and Challenges in the Emergence of Novel Coronavirus Using Interactions in Uber Drivers Forum
The outbreak and emergence of the novel coronavirus (COVID-19) pandemic affected every aspect of human activity, especially the transportation sector. Many cities adopted unprecedented lockdown strategies that resulted in significant nonessential mobility restrictions; hence, transportation network companies (TNCs) have experienced major shifts in their operation. Millions of people alone in the USA have filed for unemployment in the early stage of the COVID-19 outbreak, many belonging to self-employed groups such as Uber/Lyft drivers. Due to unprecedented scenarios, both drivers and passengers experienced overwhelming challenges that might elongate the recovery process. The goal of this study is to understand the risk, response, and challenges associated with ridesharing (TNCs, drivers, and passengers) during the COVID-19 pandemic situation. As such, large-scale crowdsourced data were collected from online ridesharing forums (i.e., Uber Drivers) since the emergence of COVID-19 (January 25–May 10, 2020). Word bigrams, word frequency heatmaps, and topic models are among the different natural language processing and text-mining techniques used to preprocess the data and classify risk perception, risk-taking, or risk-averting behaviors associated with ridesharing during a major disease outbreak. Results indicate higher levels of concern about economic disruption, availability of stimulus checks, new employment opportunities, hospitalization, pandemic, personal hygiene, and staying at home. In addition, unprecedented challenges due to unemployment and the risk and uncertainties in the required personal protective actions against spreading the disease due to sharing are among the major interactions. The proposed text-based data analytics of the ridesharing risk communication dynamics during this pandemic will help to identify unobserved factors inadvertently affecting the TNCs as well as the users (drivers and passengers) and identify more efficient strategies and alternatives for the forthcoming “new normal” of the current pandemic and the ones in the future. The study will also guide us toward understanding how efficiently online social interaction outlets can be designed and implemented more effectively during a major crisis and how to leverage such platforms for providing guidelines during emergencies to minimize transmission of disease due to shared travel.
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- NSF-PAR ID:
- 10215933
- Date Published:
- Journal Name:
- Frontiers in Built Environment
- Volume:
- 7
- ISSN:
- 2297-3362
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fbuil.2021.619283
