Conference proceeding
Determining User Similarity in Healthcare Social Media Using Content Similarity and Structural Similarity
ARTIFICIAL INTELLIGENCE IN MEDICINE (AIME 2015), v 9105, pp 216-226
01 Jan 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
More and more health consumers discuss healthcare topics with peers in online health social websites. These health social websites empower consumers to actively participate in their own healthcare and promotes communication between people. However, it is difficult for consumers to find information efficiently from hundreds of thousands of discussion threads. Finding similar users for consumers enables them to see what their peers are doing or experiencing thus enables automated selection of "relevant" information. In this work, we proposed two different methods for computing user similarity in healthcare social media using content and structural information respectively. Experiment results showed that the method using structural information from a heterogeneous healthcare information network performed better than content similarity in finding active similar users. However, when the users are not as active or contributing relatively fewer messages in social media, content similarity performed better in identifying these users.
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Details
- Title
- Determining User Similarity in Healthcare Social Media Using Content Similarity and Structural Similarity
- Creators
- Ling Jiang - Drexel UniversityChristopher C. Yang - Drexel University
- Contributors
- J H Holmes (Editor)R Bellazzi (Editor)L Sacchi (Editor)N Peek (Editor)
- Publication Details
- ARTIFICIAL INTELLIGENCE IN MEDICINE (AIME 2015), v 9105, pp 216-226
- Series
- Lecture Notes in Artificial Intelligence
- Publisher
- Springer Nature
- Number of pages
- 11
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000364534300028
- Scopus ID
- 2-s2.0-84947928179
- Other Identifier
- 991019169642904721
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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, Interdisciplinary Applications
- Medical Informatics
- Robotics