ArticlePloS one2014
Engagement with health agencies on twitter.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled 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.
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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating the Effectiveness of Internet-Based Communication for Public Health: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Attention-Seeking Strategies: An Investigation of Sexual Assault Organizations' Communication Tactics on Twitter.Nonprofit and voluntary sector quarterly · 2025Article
- Promoting Public Engagement in Palliative and End-of-Life Care Discussions on Chinese Social Media: Model Development and Analysis.Journal of medical Internet research · 2025Article
- Advocating for Older Adults in the Age of Social Media: Strategies to Achieve Peak Engagement on Twitter.JMIR aging · 2024Article
- Article
- Interactions between emotional and cognitive engagement with science on YouTube.Public understanding of science (Bristol, England) · 2021Article
- Texas Public Agencies' Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach.JMIR public health and surveillance · 2021Article
- How do sentiments affect virality on Twitter?Royal Society open science · 2021Article
- Examining Tweet Content and Engagement of Canadian Public Health Agencies and Decision Makers During COVID-19: Mixed Methods Analysis.Journal of medical Internet research · 2021Article
- Use of #SaludTues Tweetchats for the Dissemination of Culturally Relevant Information on Latino Health Equity: Exploratory Case Study.JMIR public health and surveillance · 2021Article
- Going rogue: what scientists can learn about Twitter communication from "alt" government accounts.PeerJ · 2021Article
- Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach.Journal of medical Internet research · 2020Article
- The Development of a Smart Health Awareness Message Framework Based on the Use of Social Media: Quantitative Study.Journal of medical Internet research · 2020Article
- Could early tweet counts predict later citation counts? A gender study in Life Sciences and Biomedicine (2014-2016).PloS one · 2020Article
- Trustworthy Health-Related Tweets on Social Media in Saudi Arabia: Tweet Metadata Analysis.Journal of medical Internet research · 2019Article
- Diabetes on Twitter: A Sentiment Analysis.Journal of diabetes science and technology · 2019Article
- Sentiment Analysis of Health Care Tweets: Review of the Methods Used.JMIR public health and surveillance · 2018Review
- "My Invisalign experience": content, metrics and comment sentiment analysis of the most popular patient testimonials on YouTube.Progress in orthodontics · 2018Article
- Current Social Media Conversations about Genetics and Genomics in Health: A Twitter-Based Analysis.Public health genomics · 2018Article
- Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter.PloS one · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate factors associated with engagement of U.S. Federal Health Agencies via Twitter. Our specific goals are to study factors related to a) numbers of retweets, b) time between the agency tweet and first retweet and c) time between the agency tweet and last retweet.
methodsWe collect 164,104 tweets from 25 Federal Health Agencies and their 130 accounts. We use negative binomial hurdle regression models and Cox proportional hazards models to explore the influence of 26 factors on agency engagement. Account features include network centrality, tweet count, numbers of friends, followers, and favorites. Tweet features include age, the use of hashtags, user-mentions, URLs, sentiment measured using Sentistrength, and tweet content represented by fifteen semantic groups.
resultsA third of the tweets (53,556) had zero retweets. Less than 1% (613) had more than 100 retweets (mean = 284). The hurdle analysis shows that hashtags, URLs and user-mentions are positively associated with retweets; sentiment has no association with retweets; and tweet count has a negative association with retweets. Almost all semantic groups, except for geographic areas, occupations and organizations, are positively associated with retweeting. The survival analyses indicate that engagement is positively associated with tweet age and the follower count.
conclusionsSome of the factors associated with higher levels of Twitter engagement cannot be changed by the agencies, but others can be modified (e.g., use of hashtags, URLs). Our findings provide the background for future controlled experiments to increase public health engagement via Twitter.
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.