ArticleJournal of biomedical informatics2013
Text classification for assisting moderators in online health communities.
Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Emotion Expression in Breast Cancer Support Seeking: Empirical Study of an Online Community.JMIR medical informatics · 2026Article
- Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum.Journal of medical Internet research · 2023Article
- When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification.BMC medical informatics and decision making · 2022Article
- Microaggression clues from social media: revealing and counteracting the suppression of women's health care.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- "I Want to Be Stepping in More" - Professional Online Forum Moderators' Experiences of Supporting Individuals in a Suicide Crisis.Frontiers in psychiatry · 2022Article
- Lightme: analysing language in internet support groups for mental health.Health information science and systems · 2020Article
- A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums.Journal of medical systems · 2020Article
- Classification of Health-Related Social Media Posts: Evaluation of Post Content-Classifier Models and Analysis of User Demographics.JMIR public health and surveillance · 2020Article
- Sentiment Analysis in Health and Well-Being: Systematic Review.JMIR medical informatics · 2020Review
- Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications.Yearbook of medical informatics · 2019Review
- Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications.Yearbook of medical informatics · 2019Review
- Online health community experiences of sexual minority women with cancer.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Diabetes on Twitter: A Sentiment Analysis.Journal of diabetes science and technology · 2019Article
- Improving Moderator Responsiveness in Online Peer Support Through Automated Triage.Journal of medical Internet research · 2019Article
- Understanding User Experience: Exploring Participants' Messages With a Web-Based Behavioral Health Intervention for Adolescents With Chronic Pain.Journal of medical Internet research · 2019Article
- Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum.Journal of medical Internet research · 2018Article
- Detecting clinically related content in online patient posts.Journal of biomedical informatics · 2017Article
- A comparison of rule-based and machine learning approaches for classifying patient portal messages.International journal of medical informatics · 2017Article
- What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer.JMIR medical informatics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectivesPatients increasingly visit online health communities to get help on managing health. The large scale of these online communities makes it impossible for the moderators to engage in all conversations; yet, some conversations need their expertise. Our work explores low-cost text classification methods to this new domain of determining whether a thread in an online health forum needs moderators' help.
methodsWe employed a binary classifier on WebMD's online diabetes community data. To train the classifier, we considered three feature types: (1) word unigram, (2) sentiment analysis features, and (3) thread length. We applied feature selection methods based on χ² statistics and under sampling to account for unbalanced data. We then performed a qualitative error analysis to investigate the appropriateness of the gold standard.
resultsUsing sentiment analysis features, feature selection methods, and balanced training data increased the AUC value up to 0.75 and the F1-score up to 0.54 compared to the baseline of using word unigrams with no feature selection methods on unbalanced data (0.65 AUC and 0.40 F1-score). The error analysis uncovered additional reasons for why moderators respond to patients' posts. DISCUSSION: We showed how feature selection methods and balanced training data can improve the overall classification performance. We present implications of weighing precision versus recall for assisting moderators of online health communities. Our error analysis uncovered social, legal, and ethical issues around addressing community members' needs. We also note challenges in producing a gold standard, and discuss potential solutions for addressing these challenges.
conclusionSocial media environments provide popular venues in which patients gain health-related information. Our work contributes to understanding scalable solutions for providing moderators' expertise in these large-scale, social media environments.
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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.