Abstract
Community-driven question-answering (CQA) services on the Internet let users share content in the form of questions and answers. Usually, questions attract multiple answers of varying quality from other users. A new approach aims to identify high-quality answers from candidate answers to questions that are semantically similar to the new question. Toward that end, the authors developed and tested a quality framework comprising social, textual, and content-appraisal features of user-generated answers in CQA services. Logistic-regression analysis revealed that content-appraisal features were the strongest predictor of quality. These features include dimensions such as comprehensiveness, truthfulness, and practicality. [ABSTRACT FROM AUTHOR] .
| Original language | English |
|---|---|
| Pages (from-to) | 66-71 |
| Number of pages | 6 |
| Journal | IEEE Internet Computing |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |