A Classification Framework for Online Social Support using Deep Learning
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.
Recommended Citation
Chen, L. (2019). A Classification Framework for Online Social Support using Deep Learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11589 LNCS, pp. 178-188. Springer Verlag.
The definitive version is available at https://doi.org/10.1007/978-3-030-22338-0_14
Meeting Name
6th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019 (2019: Jul. 26-31, Orlando, FL)
Department(s)
Business and Information Technology
Keywords and Phrases
Deep learning; Doc2vec; Machine learning; Online health communities; Social support; Word embedding
International Standard Book Number (ISBN)
978-303022337-3
International Standard Serial Number (ISSN)
0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Springer Verlag, All rights reserved.
Publication Date
01 Jul 2019
