ArticleJournal of medical Internet research2021
Using Tweets to Understand How COVID-19-Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- The role of social media on COVID-19 preventive behaviors worldwide, systematic review.PloS one · 2024Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Evaluation of a Canadian social media platform for communicating perinatal health information during a pandemic.PLOS digital health · 2025Article
- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation.Heliyon · 2024Article
- Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.PloS one · 2024Article
- Patterns of diverse and changing sentiments towards COVID-19 vaccines: a sentiment analysis study integrating 11 million tweets and surveillance data across over 180 countries.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Using Patient-Reported Health Data From Social Media to Identify Diverse Lupus Patients and Assess Their Symptom and Medication Expressions: A Feasibility Study.Arthritis care & research · 2023Article
- Futile or fertile? The effect of persuasive strategies on citizen engagement in COVID-19 vaccine-related tweets across six national health departments.Social science & medicine (1982) · 2023Article
- Health belief model and social media engagement: A cross-national study of health promotion strategies against COVID-19 in 2020.Frontiers in public health · 2023Article
- Living with a Pandemic from Psycho-Social Perspectives: A Narrative Review.Iranian journal of public health · 2022Review
- An easy numeric data augmentation method for early-stage COVID-19 tweets exploration of participatory dynamics of public attention and news coverage.Information processing & management · 2022Article
- Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication.Cognitive computation · 2022Article
- What the public in England know about antibiotic use and resistance in 2020: a face-to-face questionnaire survey.BMJ open · 2022Article
- How News Agencies' Twitter Posts on COVID-19 Vaccines Attract Audiences' Twitter Engagement: A Content Analysis.International journal of environmental research and public health · 2022Article
- Conflicting attitudes: Analyzing social media data to understand the early discourse on COVID-19 passports.Technology in society · 2022Article
- Application of Decision Tree Intelligent Algorithm in Data Analysis of Physical Health Test.Journal of healthcare engineering · 2022Article
- Why Vaccinate Against COVID-19? A Population-Based Survey in Switzerland.International journal of public health · 2022Article
- Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data.Journal of medical Internet research · 2021Article
- A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration.Epidemiologia (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities.
objectiveThis study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media.
methodsWe used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures.
resultsThe machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R
conclusionsAs an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.