Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users’ perceptions
Name
Whether_the_Weather_Will_Help_Us_Weather_the_COVID.pdf
Size
2.29 MB
Format
Adobe PDF
Checksum (MD5)
a1bcbc275ec25a333eac38b4a5bbe591
Author(s)
Gupta, Marichi
Bansal, Aditya
Jain, Bhav
Rochelle, Jillian
Oak, Atharv
Jalali, Seyed Mohammad Javad
Date Issued
January 2021
Journal
International Journal of Medical Informatics
Publisher
Elsevier BV
Citation
Gupta, Marichi et al. "Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users’ perceptions." International Journal of Medical Informatics 145 (January 2021): 104340 © 2020 Elsevier B.V.
Version
Original manuscript
Abstract
Objective: The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals’ perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users’ perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time.
Materials and Methods:
We collected 166,005 English tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion.
Results: We identified 28,555 relevant tweets and estimate that 40.4 % indicate uncertainty about weather’s impact, 33.5 % indicate no effect, and 26.1 % indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion.
Discussion: There is no consensus among the public for weather’s potential impact. Earlier months were characterized by tweets that were uncertain of weather’s effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenza’s seasonality, President Trump’s comments on weather’s effect, and social distancing.
Conclusion: We exhibit a research approach that is effective in measuring population perceptions and identifying misconceptions, which can inform public health communications.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.ijmedinf.2020.104340
