Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users' perceptions.

Marichi Gupta, Aditya Bansal, Bhav Jain, Jillian Rochelle, Atharv Oak, Mohammad S Jalali

Abstract read

In one paragraph

Article in International journal of medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it

0cells of the map it votes in

6citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

Risk identification and assessment of Internet public opinion on public emergencies based on Bayesian network and association rule mining.Frontiers in public health · 2025
Article
Annotated dataset for sentiment analysis and sarcasm detection: Bilingual code-mixed English-Malay social media data in the public security domain.Data in brief · 2024
Article
Identifying and Characterizing Medical Advice-Seekers on a Social Media Forum for Buprenorphine Use.International journal of environmental research and public health · 2022
Article
Multi-stage Internet public opinion risk grading analysis of public health emergencies: An empirical study on Microblog in COVID-19.Information processing & management · 2022
Article
Coronavirus infections and deaths by poverty status: The effects of social distancing.Journal of economic behavior & organization · 2021
Article
COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi.PloS one · 2021
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Marichi GuptaMGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Aditya BansalMGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Indian Institute of Technology Delhi, New Delhi, Delhi, India.

Bhav JainMGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA.

Jillian RochelleMGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Northwestern University, Evanston, IL, USA.

Atharv OakMGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA.

Mohammad S JalaliMGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: msjalali@mgh.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe 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

methodsWe 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.

resultsWe 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.

conclusionWe exhibit a research approach that is effective in measuring population perceptions and identifying misconceptions, which can inform public health communications.

Indexed as

COVID-19Machine LearningSocial MediaWeatherHumansPandemicsSARS-CoV-2Individuals’ perceptionsMachine learningOpinion miningSARS-CoV-2 transmissionTopic modeling

Identifiers

PMID33242762

DOI10.1016/j.ijmedinf.2020.104340

PMCPMC7654388

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.