ArticlePublic health genomics2018
Current Social Media Conversations about Genetics and Genomics in Health: A Twitter-Based Analysis.
Article in Public health genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Perception of the Importance of Inherited Genetics and Lifestyle on the Development of Chronic Diseases: A UK Cross-Sectional Survey.Health science reports · 2025Article
- Characterizing Precision Nutrition Discourse on Twitter: Quantitative Content Analysis.Journal of medical Internet research · 2023Article
- Uncertainty in healthcare and health decision making: Five methodological and conceptual research recommendations from an interdisciplinary team.Journal of behavioral medicine · 2023Review
- Mendelian or Multifactorial? Current Undergraduate Genetics Assessments Focus on Genes and Rarely Include the Environment.Journal of microbiology & biology education · 2022Article
- An in-depth exploration of the post-test informational needs ofHereditary cancer in clinical practice · 2020Article
- Personalized Medicine Implementation with Non-traditional Data Sources: A Conceptual Framework and Survey of the Literature.Yearbook of medical informatics · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundThe growing availability of genomic information to the public may spur discussion about genetics and genomics on social media. Sites, including Twitter, provide a unique space for the public to access and discuss health information. The objective of this study was to better understand how social media users are sharing information about genetics and genomics in health and healthcare and what information is most commonly discussed among Twitter users.
methodsWe obtained tweets with specific genetics- and genomics-related keywords from Crimson Hexagon. We used Boolean logic to collect tweets containing chosen keywords within the timeframe of October 1, 2016, to October 1, 2017. Features of the software were used to identify salient themes in conversation, conduct an emergent content analysis, and gather key demographic information.
resultsWe obtained 347,196 tweets from our search. There was a monthly average volume of 28,432 tweets. The five categories of tweets included: genetic disorders/disease (45.3%), health (15.6%), genomics (8%), and genetic testing (7.3%). Top influencers in the conversation included news outlets and universities.
conclusionsThis content analysis provides insight about the types of conversation related to genomics and health. Conversations about genomics are occurring on Twitter, and they frequently emphasize rare genetic diseases and genetic disorders. These discussions tend to be driven by key influencers who primarily include news media outlets. Further understanding of the discussions related to genomics and health in social media may offer insight about topics of importance to the public.
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.