Innovations in news media: Crisis classification system

David Kaczynski, Lisa Gandy, Gongzhu Hu

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

1 Scopus citations


Research in crisis management is a relatively new area of study, originating in the 1980s. Researchers have created several different models that separate organizational crises into discrete stages, such as pre-crisis, crisis and post-crisis. In this article we discuss a natural language based crisis detection system which classifies news articles relating to crises into the appropriate crisis stage. We use news articles from the New York Times as a source of training data, and use this data along with state of the art data mining and machine learning algorithms as the core of the system. In the future, our system may be expanded to identify and evaluate crisis management strategies, suggest crisis management strategies for the current state of a crisis, or provide stakeholders with summaries of crises in news media.

Original languageEnglish
Title of host publicationAdvances in Data Mining
Subtitle of host publicationApplications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783319415604
StatePublished - 2016
Event16th Industrial Conference on Advances in Data Mining, ICDM 2016 - New York, United States
Duration: Jul 13 2016Jul 17 2016

Publication series

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


Conference16th Industrial Conference on Advances in Data Mining, ICDM 2016
Country/TerritoryUnited States
CityNew York


  • Crisis management
  • Data mining
  • Machine learning


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