图书情报工作

图书情报工作 >

2013 , Vol. 57 >Issue 23: 114 - 120

DOI: https://doi.org/10.7536/j.issn.0252-3116.2013.23.019

知识组织

微博谣言识别研究

  • 贺刚 ,
  • 吕学强 ,
  • 李卓 ,
  • 徐丽萍
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  • 1. 北京信息科技大学网络文化与数字传播北京市重点实验室;
    2. 北京城市系统工程研究中心
贺刚，北京信息科技大学网络文化与数字传播北京市重点实验室硕士研究生，E-mail：hegang_126@126.com；吕学强，北京信息科技大学网络文化与数字传播北京市重点实验室教授，博士；李卓，北京信息科技大学网络文化与数字传播北京市重点实验室讲师，博士；徐丽萍，北京城市系统工程研究中心副研究员，博士。

收稿日期: 2013-09-17

  修回日期: 2013-11-15

  网络出版日期: 2013-12-05

基金资助

本文系国家自然科学基金项目“网页内容真实性评价研究、基于本体的专利自动标引研究”（项目编号：61271304）和北京市教委科技发展计划重点项目暨北京市自然科学基金B类重点项目“面向领域的互联网多模态信息精准搜索方法研究”（项目编号：KZ201311232037）研究成果之一。

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Automatic Rumor Identification on Microblog

  • He Gang ,
  • Lü Xueqiang ,
  • Li Zhuo ,
  • Xu Liping
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  • 1. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101;
    2. Beijing Research Center of Urban System Engineering, Beijing 100089

Received date: 2013-09-17

  Revised date: 2013-11-15

  Online published: 2013-12-05

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摘要

指出微博在传播信息的同时，也夹杂着谣言等虚假消息、不实言论。针对微博谣言传播速度快、影响范围广等特点，深层挖掘微博中的隐含信息，提出符号特征、链接特征、关键词分布特征和时间差等新特征，将微博谣言识别形式化为分类问题，综合新提取的特征与微博文本特征、用户特征和传播特征构建多个特征模板，利用SVM分类学习方法对微博进行分类，识别结果可有效辅助人们更好、更快地识别谣言。实验结果表明，在基本特征的基础之上，新提出的特征能有效提高微博谣言识别的正确率。

关键词： 微博; 谣言识别; 特征模板; SVM

本文引用格式

贺刚 , 吕学强 , 李卓 , 徐丽萍 . 微博谣言识别研究[J]. 图书情报工作, 2013 , 57(23) : 114 -120 . DOI: 10.7536/j.issn.0252-3116.2013.23.019

Abstract

Microblog not only disseminates information, but also is mingled with rumors and false news. In view of microblog rumors rapidly spreading with wide scope of influence, new features such as symbol, links, keywords distribution and delta-T are proposed by deeply mining the feature information implied in microblog. Rumor identification is formulated as classification problem. Different feature templates are built with new proposed features and classic features like text features, user features and propagation features of microblog. Then SVM is used to classify microblog to help effectively identify rumors. The experimental results suggest that the new features proposed based on the basic ones significantly promotes the overall accuracy of rumor identification.

Key words： microblog; rumor identification; feature template; SVM

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