Evidence map›Paper›PMID 38557446›Full record

ArticleJMIR infodemiology2024

Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences.

Masaru Kamba, Wan Jou She, Kiki Ferawati, Shoko Wakamiya, Eiji Aramaki

Erratum issuedAbstract read
In one paragraph

Article in JMIR infodemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Masaru KambaDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.ORCID 0000-0003-3691-9935
Wan Jou SheDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.ORCID 0000-0002-1549-5777
Kiki FerawatiDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.ORCID 0000-0003-0717-0769
Shoko WakamiyaDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.ORCID 0000-0002-9371-1340
Eiji AramakiDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.ORCID 0000-0003-0201-3609

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite being a pandemic, the impact of the spread of COVID-19 extends beyond public health, influencing areas such as the economy, education, work style, and social relationships. Research studies that document public opinions and estimate the long-term potential impact after the pandemic can be of value to the field.

objectiveThis study aims to uncover and track concerns in Japan throughout the COVID-19 pandemic by analyzing Japanese individuals' self-disclosure of disruptions to their life plans on social media. This approach offers alternative evidence for identifying concerns that may require further attention for individuals living in Japan.

methodsWe extracted 300,778 tweets using the query phrase Corona-no-sei ("due to COVID-19," "because of COVID-19," or "considering COVID-19"), enabling us to identify the activities and life plans disrupted by the pandemic. The correlation between the number of tweets and COVID-19 cases was analyzed, along with an examination of frequently co-occurring words.

resultsThe top 20 nouns, verbs, and noun plus verb pairs co-occurring with Corona no-sei were extracted. The top 5 keywords were graduation ceremony, cancel, school, work, and event. The top 5 verbs were disappear, go, rest, can go, and end. Our findings indicate that education emerged as the top concern when the Japanese government announced the first state of emergency. We also observed a sudden surge in anxiety about material shortages such as toilet paper. As the pandemic persisted and more states of emergency were declared, we noticed a shift toward long-term concerns, including careers, social relationships, and education.

conclusionsOur study incorporated machine learning techniques for disease monitoring through the use of tweet data, allowing the identification of underlying concerns (eg, disrupted education and work conditions) throughout the 3 stages of Japanese government emergency announcements. The comparison with COVID-19 case numbers provides valuable insights into the short- and long-term societal impacts, emphasizing the importance of considering citizens' perspectives in policy-making and supporting those affected by the pandemic, particularly in the context of Japanese government decision-making.

Indexed as

COVID-19Social MediaHumansJapanPandemicsSARS-CoV-2concernsCOVID-19disrupted plansnatural language processingNLPTwitter

Identifiers

PMID38557446
PMCPMC10986681

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LicenceCC BY
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Registered trials

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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.