Impact of the COVID-19 Pandemic on Scoial Media Use: Evidence from Scopus Database
Bibliographic record
Abstract
Background: A cademia has come to focus more on the rising influence of social media during the Covid-19 pandemic, manily in response to its effects. The present study systematically examines global research trends over the past 6 years on the influence of the Covid-19 pandemic on social media use. Method: We searched the Scopus database for literature published during the year (2019–2024) regarding the worldwide effects of the Covid-19 pandemic on social media use. Data visualization and analysis were performed using R-Studio, VOSviewer, GraphPad Prism (version 5), and OriginPro 8 (OriginLab Corp.) to analyze bibliometric data and produce tables and figures. Results: Of 1,192 documents identified through searching Scopus databases, including 1172 Articles and 20 reviews, were retrieved between 2019 and 2024. The number of publications is growing annually, reaching over 300 in 2021 and 2022. The countries with the most published articles were the United States, the United Kingdom, India, Spain, and China. University of Toronto, National University of Singapore, University College London, University of Pennsylvania, and Harvard Medical School have the highest output. The Journal of Medical Internet Research is the leading and most common journal in terms of the number of publications and citations in the field. As for authors, WANG Y from the School of Public Health, Fudan University, Fudan Institute of Health Communication, Shanghai, Chin has the highest number of published articles. Amgen is the funding agency. The National Institute of Health is among the top funding agencies, followed by UK Research and Innovation, which funded most of the research. According to keyword analysis, "social media," "Covid-19", "human," "pandemic," and "humans" are the five keywords Plus with the highest frequency of co-occurrence, where "Covid-19", "Twitter," "social media," "sentiment analysis," and "coronavirus," with the highest frequency of Authors Keywords co-occurrence. Conclusion: The Covid-19 pandemic has strongly influenced worldwide social media research and publications in the past six years. An increase in the emphasis on behavior and machine learning studies, as identified through keyword analysis, reflects a shift towards an interdisciplinary study of the impacts related to the pandemic. Interest in the subject shifted from general topics of Covid-19 to specific fields like public health and computational methods. Twitter has been a key data provider, particularly in vaccine research, to help inform and shape existing policy discussions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.084 | 0.103 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".