Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment

Recently, health social media have engaged more and more people to share their personal feelings, opinions, and experience in the context of health informatics, which has drawn increasing attention from both academia and industry. In this paper, we focus on the behavioral influence analysis based on...

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Bibliographic Details
Published in:IEEE transactions on computational social systems 2019-10, Vol.6 (5), p.888-897
Main Authors: Zhou, Xiaokang, Liang, Wei, Wang, Kevin I-Kai, Shimizu, Shohei
Format: Article
Language:English
Subjects:
Algorithms
Analytical models
Artificial neural networks
Behavioral analysis
Data mining
Data models
Digital media
Feature extraction
Health
health social media
Neural networks
personalized recommendation
Public healthcare
social influence
Social networking (online)
Social networks
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Summary:Recently, health social media have engaged more and more people to share their personal feelings, opinions, and experience in the context of health informatics, which has drawn increasing attention from both academia and industry. In this paper, we focus on the behavioral influence analysis based on heterogeneous health data generated in social media environments. An integrated deep neural network (DNN)-based learning model is designed to analyze and describe the latent behavioral influence hidden across multiple modalities, in which a convolutional neural network (CNN)-based framework is used to extract the time-series features within a certain social context. The learned features based on cross-modality influence analysis are then trained in a SoftMax classifier, which can result in a restructured representation of high-level features for online physician rating and classification in a data-driven way. Finally, two algorithms within two representative application scenarios are developed to provide patients with personalized recommendations in health social media environments. Experiments using the real world data demonstrate the effectiveness of our proposed model and method.
ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2019.2918285