ArticleJournal of medical Internet research2019
Mining of Textual Health Information from Reddit: Analysis of Chronic Diseases With Extracted Entities and Their Relations.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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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.
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Who cites it
32 citing papers in PubMed.
- Gestational diabetes mellitus and disordered eating behaviours: A qualitative study of online forum discussions.Diabetic medicine : a journal of the British Diabetic Association · 2026Article
- Public perceptions of AI in healthcare: a large-scale BERTopic and sentiment analysis of Reddit discussions.Frontiers in public health · 2026Article
- Article
- "I miss stars, too": a thematic analysis of the experiences of persons with retinitis pigmentosa using Reddit.Journal of community genetics · 2025Article
- Health Care Content and Engagement in Chronic Illness Instagram Posts: Content Analysis.JMIR formative research · 2025Article
- Public Perspectives on Palliative and Hospice Care: Social Media Content Analysis Using Topic Modeling and Multiclass Sentiment Analysis.Journal of medical Internet research · 2025Article
- Sentiment Analysis of Transsphenoidal Surgery in the Cushing's Subreddit.Journal of neurological surgery. Part B, Skull base · 2025Article
- Article
- Reddit users' questions and concerns about glaucoma.International ophthalmology · 2025Article
- Understanding Loneliness Through Analysis of Twitter and Reddit Data: Comparative Study.Interactive journal of medical research · 2025Article
- Patient Information Needs and Decision-Making Before a Cardiac Implantable Electronic Device: A Qualitative Study Utilizing Social Media Data.Journal of clinical psychology in medical settings · 2025Article
- Understanding Health-Related Discussions on Reddit: Development of a Topic Assignment Method and Exploratory Analysis.JMIR formative research · 2025Article
- Combining Topic Modeling, Sentiment Analysis, and Corpus Linguistics to Analyze Unstructured Web-Based Patient Experience Data: Case Study of Modafinil Experiences.Journal of medical Internet research · 2024Article
- Automated information extraction model enhancing traditional Chinese medicine RCT evidence extraction (Evi-BERT): algorithm development and validation.Frontiers in artificial intelligence · 2024Article
- Comparing Literature- and Subreddit-Derived Laboratory Values in Polycystic Ovary Syndrome (PCOS): Validation of Clinical Data Posted on PCOS Reddit Forums.JMIR formative research · 2023Article
- Disruptions in the Cystic Fibrosis Community's Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments.Journal of medical Internet research · 2023Article
- The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts.Frontiers in public health · 2023Article
- Identifying mental health discussion topic in social media community: subreddit of bipolar disorder analysis.Frontiers in research metrics and analytics · 2023Article
- Informational Justice and Remote Working: All is Not Fair for Work at Home.Employee responsibilities and rights journal · 2022Article
- Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing.JMIR formative research · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSocial media platforms constitute a rich data source for natural language processing tasks such as named entity recognition, relation extraction, and sentiment analysis. In particular, social media platforms about health provide a different insight into patient's experiences with diseases and treatment than those found in the scientific literature.
objectiveThis paper aimed to report a study of entities related to chronic diseases and their relation in user-generated text posts. The major focus of our research is the study of biomedical entities found in health social media platforms and their relations and the way people suffering from chronic diseases express themselves.
methodsWe collected a corpus of 17,624 text posts from disease-specific subreddits of the social news and discussion website Reddit. For entity and relation extraction from this corpus, we employed the PKDE4J tool developed by Song et al (2015). PKDE4J is a text mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework.
resultsUsing PKDE4J, we extracted 2 types of entities and relations: biomedical entities and relations and subject-predicate-object entity relations. In total, 82,138 entities and 30,341 relation pairs were extracted from the Reddit dataset. The most highly mentioned entities were those related to oncological disease (2884 occurrences of cancer) and asthma (2180 occurrences). The relation pair anatomy-disease was the most frequent (5550 occurrences), the highest frequent entities in this pair being cancer and lymph. The manual validation of the extracted entities showed a very good performance of the system at the entity extraction task (3682/5151, 71.48% extracted entities were correctly labeled).
conclusionsThis study showed that people are eager to share their personal experience with chronic diseases on social media platforms despite possible privacy and security issues. The results reported in this paper are promising and demonstrate the need for more in-depth studies on the way patients with chronic diseases express themselves on social media platforms.
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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.