Understanding User Intents in Online Health Forums

Thomas Zhang, Jason H.D. Cho, Chengxiang Zhai

Research output: Contribution to journalArticlepeer-review


Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding user intents would enable forums to find and recommend relevant information to users by filtering out threads that do not match particular intents. In this paper, we derive a taxonomy of intents to capture user information needs in online health forums and propose novel pattern-based features for use with a multiclass support vector machine (SVM) classifier to classify original thread posts according to their underlying intents. Since no dataset existed for this task, we employ three annotators to manually label a dataset of 1192 HealthBoards posts spanning four forum topics. Experimental results show that a SVM using pattern-based features is highly capable of identifying user intents in forum posts, reaching a maximum precision of 75%, and that a SVM-based hierarchical classifier using both pattern and word features outperforms its SVM counterpart that uses only word features. Furthermore, comparable classification performance can be achieved by training and testing on posts from different forum topics.

Original languageEnglish (US)
Article number7066225
Pages (from-to)1392-1398
Number of pages7
JournalIEEE Journal of Biomedical and Health Informatics
Issue number4
StatePublished - Jul 1 2015


  • Forum intents
  • online health forums
  • pattern based features
  • support vector machines
  • user intent classification

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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