ArticleJournal of medical Internet research2017
Using Social Media Data to Understand the Impact of Promotional Information on Laypeople's Discussions: A Case Study of Lynch Syndrome.
Article in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Developing a standardized protocol for computational sentiment analysis research using health-related social media data.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Natural Language Processing and Machine Learning Techniques for Analyzing Conversations About Nutritional Yeasts in the United States and France: Retrospective Social Media Listening Study.JMIR infodemiology · 2025Article
- A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data.JMIR mental health · 2024Article
- Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis.JMIR formative research · 2023Article
- Biases in using social media data for public health surveillance: A scoping review.International journal of medical informatics · 2022Article
- Analysis of Content, Social Networks, and Sentiment of Front-of-Pack Nutrition Labeling in the European Union on Twitter.Frontiers in nutrition · 2022Article
- A scoping review of social and behavioral science research to translate genomic discoveries into population health impact.Translational behavioral medicine · 2021Article
- Data and Model Biases in Social Media Analyses: A Case Study of COVID-19 Tweets.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis.International journal of environmental research and public health · 2020Article
- Assessing mental health signals among sexual and gender minorities using Twitter data.Health informatics journal · 2020Article
- Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020Article
- Social Media Multidimensional Analysis for Intelligent Health Surveillance.International journal of environmental research and public health · 2020Article
- Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Sentiment Analysis in Health and Well-Being: Systematic Review.JMIR medical informatics · 2020Review
- Mining Twitter to Assess the Determinants of Health Behavior towards Palliative Care in the United States.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- ML-Net: multi-label classification of biomedical texts with deep neural networks.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter.Studies in health technology and informatics · 2019Article
- Using Twitter to Understand the Human Bowel Disease Community: Exploratory Analysis of Key Topics.Journal of medical Internet research · 2019Article
- Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017.Journal of medical Internet research · 2019Article
- Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models.Journal of medical Internet research · 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
11 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundSocial media is being used by various stakeholders among pharmaceutical companies, government agencies, health care organizations, professionals, and news media as a way of engaging audiences to raise disease awareness and ultimately to improve public health. Nevertheless, it is unclear what effects this health information has on laypeople.
objectiveThis study aimed to provide a detailed examination of how promotional health information related to Lynch syndrome impacts laypeople's discussions on a social media platform (Twitter) in terms of topic awareness and attitudes.
methodsWe used topic modeling and sentiment analysis techniques on Lynch syndrome-related tweets to answer the following research questions (RQs): (1) what are the most discussed topics in Lynch syndrome-related tweets?; (2) how promotional Lynch syndrome-related information on Twitter affects laypeople's discussions?; and (3) what impact do the Lynch syndrome awareness activities in the Colon Cancer Awareness Month and Lynch Syndrome Awareness Day have on laypeople's discussions and their attitudes? In particular, we used a set of keywords to collect Lynch syndrome-related tweets from October 26, 2016 to August 11, 2017 (289 days) through the Twitter public search application programming interface (API). We experimented with two different classification methods to categorize tweets into the following three classes: (1) irrelevant, (2) promotional health information, and (3) laypeople's discussions. We applied a topic modeling method to discover the themes in these Lynch syndrome-related tweets and conducted sentiment analysis on each layperson's tweet to gauge the writer's attitude (ie, positive, negative, and neutral) toward Lynch syndrome. The topic modeling and sentiment analysis results were elaborated to answer the three RQs.
resultsOf all tweets (N=16,667), 87.38% (14,564/16,667) were related to Lynch syndrome. Of the Lynch syndrome-related tweets, 81.43% (11,860/14,564) were classified as promotional and 18.57% (2704/14,564) were classified as laypeople's discussions. The most discussed themes were treatment (n=4080) and genetic testing (n=3073). We found that the topic distributions in laypeople's discussions were similar to the distributions in promotional Lynch syndrome-related information. Furthermore, most people had a positive attitude when discussing Lynch syndrome. The proportion of negative tweets was 3.51%. Within each topic, treatment (16.67%) and genetic testing (5.60%) had more negative tweets compared with other topics. When comparing monthly trends, laypeople's discussions had a strong correlation with promotional Lynch syndrome-related information on awareness (r=.98, P<.001), while there were moderate correlations on screening (r=.602, P=.05), genetic testing (r=.624, P=.04), treatment (r=.69, P=.02), and risk (r=.66, P=.03). We also discovered that the Colon Cancer Awareness Month (March 2017) and the Lynch Syndrome Awareness Day (March 22, 2017) had significant positive impacts on laypeople's discussions and their attitudes.
conclusionsThere is evidence that participative social media platforms, namely Twitter, offer unique opportunities to inform cancer communication surveillance and to explore the mechanisms by which these new communication media affect individual health behavior and population 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.