Harnessing Artificial Intelligence to Improve the Quality of Answers in Online Question-answering Health Forums

• 成果类型: Article
• 署名作者: Mousavi, Reza; Raghu, T. S.; Frey, Keith
• 署名单位: University of Virginia; Arizona State University; Arizona State University-Tempe; Adventist Health Services; AdventHealth; CommonSpirit
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2020.1831775
• 发表日期: 2020
• 页码: 1073-1098
• 关键词: INTERNET; INFORMATION; KNOWLEDGE

摘要

Quality of answers in health-related community-based question answering (HCQA) forums has been a concern for both users and forum administrators. We conducted a two-phase study to better understand the quality of answers in HCQA forums. First, we employed machine learning to examine the quality of health content. We validated our algorithmic quality ratings by comparing them with those of two physicians. Second, using data from Yahoo! Answers Health section, we examined the effect of the quality of the first answer on the quality of the subsequent answers. Our results suggest that the quality of the subsequent answers is impacted by the quality of the first displayed answer. We further show that the impact of the first displayed answer is larger when the answerers are more familiar with the forum but smaller when the forum provides tips for answering questions. Our study helps HCQA forums to improve the overall quality of answers by 1- creating an algorithmic solution that reliably measures the quality of answers, and 2- adjusting the order of existing answers to encourage higher quality subsequent answers. Our findings also extend the applicability of the order effect to online forums and provide evidence that experienced users would be more influenced by the order effect in such forums.