An evaluation of phrasal and clustered representations on a text categorization task
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Using machine learning to augment collaborative filtering of community discussions
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
AskHERMES: An online question answering system for complex clinical questions
Journal of Biomedical Informatics
Preprocessing unbalanced data using support vector machine
Decision Support Systems
Tackling dilemmas in supporting 'the whole person' in online patient communities
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social media mining for drug safety signal detection
Proceedings of the 2012 international workshop on Smart health and wellbeing
Predicting postpartum changes in emotion and behavior via social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Editorial: Biomedical information through the implementation of social media environments
Journal of Biomedical Informatics
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Objectives: Patients 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. Methods: We 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 @g^2 statistics and under sampling to account for unbalanced data. We then performed a qualitative error analysis to investigate the appropriateness of the gold standard. Results: Using 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. Conclusion: Social 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.