Language style and recognition of the answers in health Q&A community: Moderating effects of medical terminology

Author:

Zhao Wenjun1,Meng Kai2ORCID,Sun Li1,Ma Jinhui1,Jia Zeguang3

Affiliation:

1. Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Hunan University of Technology and Business, China; Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Technology and Business, China

2. School of Digital Economy and Management, Nanjing University, China; Laboratory of Data Intelligence and Interdisciplinary Innovation, Nanjing University, China

3. Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Hunan University of Technology and Business, China

Abstract

The rise of social question and answer (Q&A) platforms has changed the model of traditional health-information services. The quality of answers on a Q&A platform is critical to attract users and increase their community engagement. Thus, recognition of the answer was used to measure users’ acceptance of the answer. A theoretical model of the impact of language style on community recognition of health questions was developed. Nearly 1330 answers from a health question from Zhihu were obtained to verify the model. Results showed that personality reliability, assertiveness, argumentation clarity, commitment, and reverse incentive of health information positively affected answer recognition, while argumentation structure negatively affected answer acceptance. Simultaneously, the degree of use of medical terminology has a significant moderating effect on the relationship between answer recognition and assertiveness, argumentation structure, argumentation clarity, and commitment. Introducing Aristotle’s rhetoric theory to language style and answer recognition could potentially develop healthy communities and disseminate health knowledge.

Funder

national social science fund of china

Publisher

SAGE Publications

Subject

Library and Information Sciences,Information Systems

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Developing a ChatGPT-Based Text Extraction Model to Analyze Effective Communication Elements in Pandemic-Related Social Q&A Responses;2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC);2024-02-19

2. Developing a GPT-Based text Extraction Model for Cancer Information;2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence);2024-01-18

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