ArticleInternational journal of public health2019
A comparison of information sharing behaviours across 379 health conditions on Twitter.
Article in International journal of public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed.
- Vaccination Conversations on X in Spanish and Catalan: Qualitative Content Analysis.JMIR infodemiology · 2025Article
- Factors influencing the continuance intention of the women's health WeChat public account: an integrated model of UTAUT2 and HBM.Frontiers in public health · 2024Article
- Sampling Twitter users for social science research: evidence from a systematic review of the literature.Quality & quantity · 2023Article
- Online health community for change: Analysis of self-disclosure and social networks of users with depression.Frontiers in psychology · 2023Article
- Exploring Public Perceptions of Dental Care Affordability in the United States: Mixed Method Analysis via Twitter.JMIR formative research · 2022Article
- Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study.JMIR medical informatics · 2022Article
- Today Is My Day: Analysis of the Awareness Campaigns' Impact on Functional Diversity in the Press, on Google, and on Twitter.International journal of environmental research and public health · 2021Article
- Online Attitudes and Information-Seeking Behavior on Autism, Asperger Syndrome, and Greta Thunberg.International journal of environmental research and public health · 2021Article
- Influencing Factors on Health Information to Improve Public Health Literacy in the Official WeChat Account of Guangzhou CDC.Frontiers in public health · 2021Article
- Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media.Journal of big data · 2021Article
- Contextualising the 2019 E-Cigarette Health Scare: Insights from Twitter.International journal of environmental research and public health · 2020Article
- A New Tool for Public Health Opinion to Give Insight Into Telemedicine: Twitter Poll Analysis.JMIR formative research · 2019Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo compare information sharing of over 379 health conditions on Twitter to uncover trends and patterns of online user activities.
methodsWe collected 1.5 million tweets generated by over 450,000 Twitter users for 379 health conditions, each of which was quantified using a multivariate model describing engagement, user and content aspects of the data and compared using correlation and network analysis to discover patterns of user activities in these online communities.
resultsWe found a significant imbalance in terms of the size of communities interested in different health conditions, regardless of the seriousness of these conditions. Improving the informativeness of tweets by using, for example, URLs, multimedia and mentions can be important factors in promoting health conditions on Twitter. Using hashtags on the contrary is less effective. Social network analysis revealed similar structures of the discussion found across different health conditions.
conclusionsOur study found variance in activity between different health communities on Twitter, and our results are likely to be of interest to public health authorities and officials interested in the potential of Twitter to raise awareness of public health.
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.