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A Classification Framework for Online Social Support Using Deep Learning

  • Langtao ChenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11589)

Abstract

Health consumers engage in social interactions in online health communities (OHCs) to seek or provide social support. Automatic classification of social support exchanged online is important for both researchers and practitioners of online health communities, especially when a large number of messages are posted on regular basis. Classification of social support in OHCs provides an efficient way to assess the effectiveness of social interactions in the virtual environment. Most previous studies of online social support classification are based on “bag-of-words” assumption and have not considered the semantic meaning of words/terms embedded in the online messages. This research proposes a classification framework for online social support using the recent development of word space models and deep learning methods. Specifically, doc2vec models, bag-of-words representations, and linguistic analysis methods are used to extract features from the text messages that are posted in OHC for online social interaction or social support exchange. Then a deep learning model is applied to classify two major types of social support (i.e., informational and emotional support) expressed in OHC reply messages.

Keywords

Online health communities Social support Machine learning Deep learning Word embedding Doc2vec 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Business and Information TechnologyMissouri University of Science and TechnologyRollaUSA

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