Book chapter
Post Classification and Recommendation for an Online Smoking Cessation Community
Public Health Intelligence and the Internet, pp 59-73
01 Jan 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
There are an increasing number of health-related communities and forums developed on the Internet, where people discuss certain health issues and exchange social support with each other. However, due to the huge amount and loose structure of user-generated content in the health communities, it is difficult for users to find relevant topics or peers to discuss with. In this paper, we focus on an online smoking cessation forum, QuitStop. We extract user discussion content from the forum, apply machine learning technology to classify posts in the forum, and develop recommendation techniques to help users find valuable topics. Using text and health feature sets, the classifiers are developed and optimized to categorize posts in terms of user intentions and social support types. The recommender systems are then developed to make a recommendation of posts to users, in which the classification results are incorporated in the neighbor-based collaborative filtering approach. It is found that the combination of text and health feature sets can achieve satisfactory classification result. Integrating classification result could help relieving cold start problem in the recommendation. It can greatly improve the recall of recommendation when limited knowledge is known for a thread.
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Details
- Title
- Post Classification and Recommendation for an Online Smoking Cessation Community
- Creators
- Mi Zhang - Drexel UniversityChristopher C. Yang - Drexel University
- Contributors
- A ShabanNejad (Editor)J S Brownstein (Editor)D L Buckeridge (Editor)
- Publication Details
- Public Health Intelligence and the Internet, pp 59-73
- Series
- Lecture Notes in Social Networks
- Publisher
- Springer Nature; VIENNA
- Number of pages
- 15
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000443333300005
- Other Identifier
- 991019168707104721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Public, Environmental & Occupational Health