Detecting anomalies in time-varying media crime news using tensor decomposition

Hugo Alatrista-Salas, Pablo Lavado, Juandiego Morzan, Miguel Nuñez-del-Prado, Gustavo Yamada

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Nowadays, the mass media surround us in many forms. Newspapers, radio and TV reports about many topics, including the crime committed in a region. Indirectly, the media provide statistics about crime incidents, and policymakers could focus their attention on the unusual number of crime news (c.f., regular events) for evaluating and proposing new public policies. In the present work, the Tensor decomposition is used to detect an unusual amount of crime news. To achieve this goal, two rejection criterion techniques were compared. Also, several image binarization techniques were used to validate our proposal. Our result can be used to detect an unusual amount of crime news as a proxy of unusual crime activity.
Original languageEnglish
Title of host publicationInformation management and big data
Subtitle of host publication6th International Conference, SIMBig 2019, Proceedings
EditorsJuan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
Place of PublicationCham
PublisherSpringer
Pages35-45
Number of pages11
ISBN (Electronic)978-3-030-46140-9
ISBN (Print)978-3-030-46139-3
DOIs
StatePublished - 23 Apr 2020
EventCommunications in Computer and Information Science -
Duration: 1 Jan 2020 → …

Publication series

NameCommunications in Computer and Information Science
Volume1070 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

ConferenceCommunications in Computer and Information Science
Period1/01/20 → …

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

  • Crime
  • Event detection
  • Tensor decomposition

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