Evidence map›Paper›PMID 33684052›Full record

ArticleJMIR medical informatics2021

Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach.

Oladapo Oyebode, Chinenye Ndulue, Ashfaq Adib, Dinesh Mulchandani, Banuchitra Suruliraj, Fidelia Anulika Orji, Christine T Chambers, Sandra Meier, Rita Orji

Open access · goldAbstract read
In one paragraph

Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
7.3field-weighted citation impact, top 2% of its field
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

24 citing papers in PubMed, 61 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. "Frontiers in health services · 2022
    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

9 authors at 2 institutions in 1 country.

Oladapo OyebodeFaculty of Computer Science, Dalhousie University, Halifax, NS, Canada.ORCID https://orcid.org/0000-0002-5797-7790
Chinenye NdulueFaculty of Computer Science, Dalhousie University, Halifax, NS, Canada.ORCID https://orcid.org/0000-0002-7050-0612
Ashfaq AdibFaculty of Computer Science, Dalhousie University, Halifax, NS, Canada.ORCID https://orcid.org/0000-0001-7887-7994
Dinesh MulchandaniFaculty of Computer Science, Dalhousie University, Halifax, NS, Canada.ORCID https://orcid.org/0000-0001-9512-5858
Banuchitra SurulirajFaculty of Computer Science, Dalhousie University, Halifax, NS, Canada.ORCID https://orcid.org/0000-0001-8016-6016
Fidelia Anulika OrjiDepartment of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.ORCID https://orcid.org/0000-0001-5282-5467
Christine T ChambersDepartment of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada.ORCID https://orcid.org/0000-0002-7138-916X
Sandra MeierDepartment of Psychiatry, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada.ORCID https://orcid.org/0000-0002-3287-5894
Rita OrjiFaculty of Computer Science, Dalhousie University, Halifax, NS, Canada.ORCID https://orcid.org/0000-0001-6152-8034
Dalhousie University · CAUniversity of Saskatchewan · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally.

objectiveThis paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data.

methodsWe applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes.

resultsA total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research.

conclusionsWe uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.

Indexed as

coronavirusCOVID-19health issuesinfodemiologyinfoveillanceinterventionsnatural language processingpsychosocial issuessocial issuessocial mediatext miningthematic analysis

Identifiers

PMID33684052
PMCPMC8025920
OpenAlexW3044533399

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.