Evidence map›Paper›PMID 39850180›Full record

ArticleCureus2024

People's Attitude and Perception of the Pandemic on Twitter: A Case Study of COVID-19 in India.

Meera Pv, Dhivya Karmegam, Suriya Saravanan

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Meera PvSchool of Public Health, SRM Institute of Science and Technology, Kattankulathur, IND.
Dhivya KarmegamSchool of Public Health, SRM Institute of Science and Technology, Kattankulathur, IND.
Suriya SaravananDepartment of Civil Engineering, Mepco Schlenk Engineering College, Sivakasi, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Understanding the attitudes and perceptions of the general population is necessary for organizing health promotion initiatives. During outbreaks, social media has a significant impact on creating social perceptions. This study aims to identify and examine the emotions expressed and topics of discussion among Indian citizens related to COVID-19 third wave, from the messages posted on Twitter using text mining techniques. Methods Twitter messages (tweets) were downloaded using Twitter API from June 1, 2021 to July 10, 2021. After pre-processing the downloaded messages, 8933 unique tweets from various individuals were taken into account for the analysis. To identify and extract emotions expressed by the people in the Twitter texts, the text mining and sentiment analysis package "syuzhet" in R software was used. In order to identify the concerns and themes of discussion by the public during the pandemic, topic analysis was done using the Latent Dirichlet Allocation (LDA) technique. To understand and measure the tweets' reachability in relation to the themes and emotions conveyed, an engagement metrics analysis was also conducted. Results According to the emotional analysis performed on Twitter messages about the COVID-19 third wave, anticipation was exhibited maximum in 4180 tweets, followed by fear in 4070, and trust in 4001 tweets. Results of topic modeling revealed that there were widespread discussions and concerns about preventative measures to deal with the COVID-19 third wave. Engagement metrics verified that the greatest number of individuals liked and retweeted tweets expressing disgust. Maximum people favorited tweets with information on preventive measures for COVID-19, and a large number of individuals re-shared messages comparing various aspects of different COVID-19 waves. Conclusion Data from social media platforms can be used to comprehend public opinions and emotions during pandemics and emergencies. This can assist public health stakeholders in managing pandemic circumstances by planning effective health communication strategies that quickly reach a larger audience. The study's findings will assist stakeholders and public health experts in effectively using social media to communicate information about COVID-19 that combat people's negative emotions and concerns. Future research can use a similar approach to comprehend people's perspectives and concerns during outbreaks and emergency circumstances.

Indexed as

covid-19 outbreakemotional analysistext miningtopic modellingtwitter

Identifiers

PMID39850180
PMCPMC11756616

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.