情报科学

• • 上一篇    下一篇

基于信息特征和信息来源的用户答案类型偏好研究:COVID-19相关问答为例

  

  1. 南京大学信息管理学院

Research on User Preferences of Answers Based on Message Features and Information Sources: Take Relevant COVID-19 Questions and Answers as Examples

  1. School of Information Management Nanjing University

摘要:

【目的/意义】社会化问答社区在健康信息传播中具有关键作用,了解用户偏好的信息类型对提高信息质量、促进与用户之间的有效沟通至关重要。【方法/过程】本研究利用多值集定性比较分析(mvQCA)方法,选取知乎中有关COVID-19的答案,从信息特征和来源的角度确定8前因变量,通过组态视角构建多重并发的因果关系,最终获取不同偏好答案对应的条件组态以及特征。。【结果/结论】研究发现:(1)偏好高的答案对应的组态有3种,其中字数多和可视化是必要条件,此外用户更偏好具有损失框架、统计证据、积极情感、可视化以及包含具体链接特征的答案;偏好低的答案对应的组态有4种,其中字数少和不包含专家是必要条件,此外用户不喜欢无框架、叙事证据、无情感、纯文本以及无具体源特征的答案;(2)偏好高的答案其主题分布在记录各国新冠时政情况、介绍新冠预防治疗、反映医患相关资讯以及热点“人物”介绍。本研究丰富了健康信息传播以及用户偏好信息类型的相关理论研究,同时对提高社会化问答社区中健康信息的整体质量以及用户之间有效沟通提供参考。

关键词:

信息特征, 信息来源, 用户偏好, COVID-19, 社会化问答社区

Abstract:

[Purpose/significance] Social Q&A communities play a key role in health information comunication, understanding user preferences of message type is crucial to improve information quality and facilitate effective communication with users. [Method/process] This study uses the multi-value set Qualitative Comparative Analysis (QCA) method to analyze relevant COVID-19 answers in Zhihu. We choose eight antecedent variables from the perspectives of message features and information sources. Then mine multiple concurrent causal relationships between variables to obtain the conditional configuration and characteristics about different user preferences of answers. [Result/conclusion] The research results showed that: (1) There are three configurations for high preference of answers, among which more numbers of words and visualization are the necessary conditions. There are 4 configurations for low preference of answers, among which less numbers of words and not containing experts are the necessary conditions, and other factors are different. (2) The topics of high preference of answers answers focus on several aspects: the current situation of each country about COVID-19, prevention and treatment ways of COVID-19, revelant information about doctor-patient special people. This study enriches the relevant theoretical research on health information communication and user preference of answers, and provides reference for improving the overall quality of health information in social Q&A communities and promoting health effective communication among users.

Key words:

message features, information source, user preference, COVID-19, social Q&A communities