图书情报工作

图书情报工作 >

2013 , Vol. 57 >Issue 15: 108 - 115

DOI: https://doi.org/10.7536/j.issn.0252-3116.2013.15.017

情报研究

从中文问答社区信息内容透视公众针对突发事件的信息需求——基于问题胶囊事件的实验研究

  • 李纲 ,
  • 陈璟浩
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  • 1. 武汉大学信息资源研究中心;
    2. 武汉大学信息管理学院
李纲,武汉大学信息资源研究中心副主任,珞珈特聘教授,博士生导师。

收稿日期: 2013-07-01

  修回日期: 2013-07-14

  网络出版日期: 2013-08-05

基金资助

本文系国家社会科学基金重大项目“面向学科领域的网络信息资源深度聚合与服务研究”(项目编号:12&ZD221)和武汉大学研究生自主科研项目“网络环境下重大突发公共卫生事件信息传播模式研究”(项目编号:2012104010203)研究成果之一。

收起

An Experimental Study to Understand the Public Information Requirements by Analyzing the Questions and Answers Posted on Online Q&A Community in Time of Public Emergency——Base on Inferior Capsule Event

  • Li Gang ,
  • Chen Jinghao
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  • 1. Center for the Studies of Information Resources of Wuhan University, Wuhan 430072;
    2. Information Management College of Wuhan University, Wuhan 430072

Received date: 2013-07-01

  Revised date: 2013-07-14

  Online published: 2013-08-05

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摘要

提出一个用于分析突发事件网民信息需求的框架,并利用网络问答社区数据进行实验分析。结果表明该框架可以有效分析突发事件网民信息需求。从信息引用情况来看,我国网民引用官方、媒体和公益组织信息来回答突发事件问题的比例很低。在突发事件中网民最关心的还是生理需求和安全需求方面的信息。本研究的意义在于为突发事件危机沟通提供相关启示。

关键词： 突发事件; 网民信息需求; 互相关分析; 文本聚类; 内容分析; 相关分析

本文引用格式

李纲 , 陈璟浩 . 从中文问答社区信息内容透视公众针对突发事件的信息需求——基于问题胶囊事件的实验研究[J]. 图书情报工作, 2013 , 57(15) : 108 -115 . DOI: 10.7536/j.issn.0252-3116.2013.15.017

Abstract

This paper first proposes an analysis framework which can be used to analyze the netizens' information requirements in public emergency. And the experimental data come from online questions and answers community. The experimental result indicated that this framework is feasible and effective. From the citing data, it can be conclude that our countries' netizens rarely cited the information from government's web site, nonprofit organizations' web site and web news in their answers. The main concern which public expressed in their posted message is physiological needs and security requirements. It is hoped that these results have some references for crisis communication.

Key words： public emergencies; netizens' information need; cross correlation; text clustering; content analysis; Spearman’s rank; correlation coefficient

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