Detecting news event from a citizen journalism website using tags

Alton Y.K. Chua, Dion Hoe Lian Goh, Khasfariyati Razikin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The accelerated news cycle and constantly emerging news-worthy events have led to 'citizen journalism' where people who are non-journalists collect, analyze and disseminate news pieces. This paper seeks to leverage tags drawn from iReport, an active citizen journalism Website to detect major news events. The goal is to examine the coverage and efficacy of news detected in iReport vis-à-vis those reported in the mainstream media. The data collection procedure involved manually culling major news events reported in Fox News between April 8 2008 and June 6 2008. Additionally, 81,815 tags from 15,216 documents were drawn from iReport during the same study period. Relative frequencies of all unique tags were used to check for spikes and bursts in the dataset. The results show that out of the 10 major news events reported in Fox News, five could be detected in iReport. This paper concludes by presenting the main findings, limitations and suggestions for future research.

Original languageEnglish
Title of host publicationActive Media Technology - 5th International Conference, AMT 2009, Proceedings
Pages478-489
Number of pages12
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event5th International Conference on Active Media Technology, AMT 2009 - Beijing, China
Duration: Oct 22 2009Oct 24 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5820 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Active Media Technology, AMT 2009
Country/TerritoryChina
CityBeijing
Period10/22/0910/24/09

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

Keywords

  • Citizen journalism
  • Event detection
  • Relative frequency
  • Social tagging
  • User-generated content

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