Abstract
Online health communities rely on information about their users to provide services to members. We partner with the online health community CaringBridge.org to infer the health condition that users are discussing from their early writing on the site. We utilize the self-reported health condition data that is provided by users to train machine learning classifiers to predict the health condition of non-reporting users. An analysis of the classifier’s errors reveals that users frequently discuss multiple health conditions. We present models with explainable features, enabling us to extract words for the enrichment of consumer health vocabularies and to support future designs connecting patients.
| Original language | English (US) |
|---|---|
| Title of host publication | CSCW 2018 Companion - Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing |
| Publisher | Association for Computing Machinery |
| Pages | 281-284 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781450360180 |
| DOIs | |
| State | Published - Oct 30 2018 |
| Event | 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2018 - Jersey City, United States Duration: Nov 3 2018 → Nov 7 2018 |
Publication series
| Name | Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW |
|---|
Other
| Other | 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2018 |
|---|---|
| Country/Territory | United States |
| City | Jersey City |
| Period | 11/3/18 → 11/7/18 |
Bibliographical note
Publisher Copyright:© 2018 Copyright is held by the owner/author(s).
Keywords
- Online Health Communities; Peer Health Support
