Information credibility on twitter in emergency situation

Xin Xia, Xiaohu Yang, Chao Wu, Shanping Li, Linfeng Bao

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

27 Citations (Scopus)

Abstract

Twitter has shown its greatest power of influence for its fast information diffusion. Previous research has shown that most of the tweets posted are truthful, but as some people post the rumors and spams on Twitter in emergence situation, the direction of public opinion can be misled and even the riots are caused. In this paper, we focus on the methods for the information credibility in emergency situation. More precisely, we build a novel Twitter monitor model to monitoring Twitter online. Within the novel monitor model, an unsupervised learning algorithm is proposed to detect the emergency situation. A collection of training dataset which includes the tweets of typical events is gathered through the Twitter monitor. Then we manually dispatch the dataset to experts who label each tweet into two classes: credibility or incredibility. With the classified tweets, a number of features related to the user social behavior, the tweet content, the tweet topic and the tweet diffusion are extracted. A supervised method using learning Bayesian Network is used to predict the tweets credibility in emergency situation. Experiments with the tweets of UK Riots related topics show that our procedure achieves good performance to classify the tweets compared with other state-of-art algorithms.

Original languageEnglish
Title of host publicationIntelligence and Security Informatics - Pacific Asia Workshop, PAISI 2012, Proceedings
PublisherSpringer
Pages45-59
Number of pages15
ISBN (Print)9783642304279
DOIs
Publication statusPublished - 18 Jun 2012
Externally publishedYes
EventPacific Asia Workshop on Intelligence and Security Informatics, PAISI 2012 - Kuala Lumpur, Malaysia
Duration: 29 May 201229 May 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7299 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePacific Asia Workshop on Intelligence and Security Informatics, PAISI 2012
CountryMalaysia
CityKuala Lumpur
Period29/05/1229/05/12

Keywords

  • Bayesian Network
  • emergency situation
  • information credibility
  • Sequential K-means
  • Twitter

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