ArticleJournal of medical Internet research2017
Understanding Health Care Social Media Use From Different Stakeholder Perspectives: A Content Analysis of an Online Health Community.
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 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it, 110 citations in OpenAlex.
- Examining the Effectiveness of Social Media for the Dissemination of Research Evidence for Health and Social Care Practitioners: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- University students doing good online: typologies, motivators, and psychological outcomes of online prosocial behavior.BMC psychology · 2026Article
- What do cancer patients discuss online regarding CINV management? A social media-based topic modeling study.Frontiers in oncology · 2026Article
- Analysis of the Behavioral Performance and Social Support of Patients in Online Health Communities From User Profile Perspectives: Comparative Study.Journal of medical Internet research · 2025Article
- Endometriosis Communities on Reddit: Quantitative Analysis.Journal of medical Internet research · 2025Article
- Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis.JMIR medical informatics · 2024Article
- Evaluating the User-Perceived Benefit of a Virtual Lung Cancer Patient Education and Support Community: LVNG With Lung Cancer.Journal of the advanced practitioner in oncology · 2024Article
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- Multiple-Perspective Data-Driven Analysis of Online Health Communities.Healthcare (Basel, Switzerland) · 2023Article
- Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review.Journal of medical Internet research · 2023Article
- Doctors ranking through heterogeneous information: The new score functions considering patients' emotional intensity.Expert systems with applications · 2023Article
- Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models.Bioengineering (Basel, Switzerland) · 2023Article
- Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis.JMIR formative research · 2023Article
- Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media.JMIR infodemiology · 2023Article
- Synergy Between Public and Private Health Care Organizations During COVID-19 on Twitter: Sentiment and Engagement Analysis Using Forecasting Models.JMIR medical informatics · 2022Article
- Doctors' Preferences in the Selection of Patients in Online Medical Consultations: An Empirical Study with Doctor-Patient Consultation Data.Healthcare (Basel, Switzerland) · 2022Article
- Online Peer Support for People With Parkinson Disease: Narrative Synthesis Systematic Review.JMIR aging · 2022Review
- Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis.International journal of environmental research and public health · 2022Article
- Framing Public Opinion on Physician-Patient Conflicts on Microblog: A Comparative Content Analysis.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHealth care social media used for health information exchange and emotional communication involves different types of users, including patients, caregivers, and health professionals. However, it is difficult to identify different stakeholders because user identification data are lacking due to privacy protection and proprietary interests. Therefore, identifying the concerns of different stakeholders and how they use health care social media when confronted with huge amounts of health-related messages posted by users is a critical problem.
objectiveWe aimed to develop a new content analysis method using text mining techniques applied in health care social media to (1) identify different health care stakeholders, (2) determine hot topics of concern, and (3) measure sentiment expression by different stakeholders.
methodsWe collected 138,161 messages posted by 39,606 members in lung cancer, diabetes, and breast cancer forums in the online community MedHelp.org over 10 years (January 2007 to October 2016) as experimental data. We used text mining techniques to process text data to identify different stakeholders and determine health-related hot topics, and then analyzed sentiment expression.
resultsWe identified 3 significantly different stakeholder groups using expectation maximization clustering (3 performance metrics: Rand=0.802, Jaccard=0.393, Fowlkes-Mallows=0.537; P<.001), in which patients (24,429/39,606, 61.68%) and caregivers (12,232/39,606, 30.88%) represented the majority of the population, in contrast to specialists (2945/39,606, 7.43%). We identified 5 significantly different health-related topics: symptom, examination, drug, procedure, and complication (Rand=0.783, Jaccard=0.369, Fowlkes-Mallows=0.495; P<.001). Patients were concerned most about symptom topics related to lung cancer (536/1657, 32.34%), drug topics related to diabetes (1883/5904, 31.89%), and examination topics related to breast cancer (8728/23,934, 36.47%). By comparison, caregivers were more concerned about drug topics related to lung cancer (300/2721, 11.03% vs 109/1657, 6.58%), procedure topics related to breast cancer (3952/13,954, 28.32% vs 5822/23,934, 24.33%), and complication topics (4449/25,701, 17.31% vs 4070/31,495, 12.92%). In addition, patients (9040/36,081, 25.05%) were more likely than caregivers (2659/18,470, 14.39%) and specialists (17,943/83,610, 21.46%) to express their emotions. However, patients' sentiment intensity score (2.46) was lower than those of caregivers (4.66) and specialists (5.14). In particular, for caregivers, negative sentiment scores were higher than positive scores (2.56 vs 2.18), with the opposite among specialists (2.62 vs 2.46). Overall, the proportion of negative messages was greater than that of positive messages related to symptom, complication, and examination. The pattern was opposite for drug and procedure topics. A trend analysis showed that patients and caregivers gradually changed their emotional state in a positive direction.
conclusionsThe hot topics of interest and sentiment expression differed significantly among different stakeholders in different disease forums. These findings could help improve social media services to facilitate diverse stakeholder engagement for health information sharing and social interaction more effectively.
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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.