Abstract
Measuring the quality of health content in online health forums has been a challenging task. The majority of the existing measures are based on nonprofessional evaluations of forum users and may not be reliable. We employed machine learning techniques, text mining methods, and Big Data platforms to construct four measures of textual quality to automatically determine the similarity of a given answer to professional answers. We then used these measures to assess the quality of 66,888 answers posted on Yahoo! Answers Health section. All four measures of textual quality revealed a higher quality for asker-selected best answers indicating that askers, to some extent, have a proper judgment to select the best answers. We also studied the presence of order effects in online health forums. Our results suggest that the textual quality of the first answer positively influences the mean textual quality of the subsequent answers and negatively influences the quantity of the subsequent answers.
| Original language | English (US) |
|---|---|
| Title of host publication | 2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015 |
| Publisher | Association for Information Systems |
| State | Published - 2015 |
| Externally published | Yes |
| Event | 2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015 - Fort Worth, United States Duration: Dec 13 2015 → Dec 16 2015 |
Other
| Other | 2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015 |
|---|---|
| Country/Territory | United States |
| City | Fort Worth |
| Period | 12/13/15 → 12/16/15 |
Keywords
- Healthcare information systems
- Information quality
- Machine learning
- Online communities
- Text mining
ASJC Scopus subject areas
- Computer Science Applications
- Statistics, Probability and Uncertainty
- Library and Information Sciences
- Applied Mathematics