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

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

doi:10.1007/978-3-030-46140-9_4

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 proceeding › Conference contribution › peer-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 language	English
Title of host publication	Information management and big data
Subtitle of host publication	6th International Conference, SIMBig 2019, Proceedings
Editors	Juan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
Place of Publication	Cham
Pages	35-45
Number of pages	11
ISBN (Electronic)	978-3-030-46140-9
DOIs	https://doi.org/10.1007/978-3-030-46140-9_4
State	Published - 23 Apr 2020
Event	Communications in Computer and Information Science - Duration: 1 Jan 2020 → …

Publication series

Name	Communications in Computer and Information Science
Volume	1070 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Communications in Computer and Information Science
Period	1/01/20 → …

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Crime
Event detection
Tensor decomposition

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-46140-9_4

Cite this

Alatrista-Salas, H., Lavado, P., Morzan, J., Nuñez-del-Prado, M., & Yamada, G. (2020). Detecting anomalies in time-varying media crime news using tensor decomposition. In J. A. Lossio-Ventura, N. Condori-Fernandez, & J. C. Valverde-Rebaza (Eds.), Information management and big data: 6th International Conference, SIMBig 2019, Proceedings (pp. 35-45). (Communications in Computer and Information Science; Vol. 1070 CCIS).. https://doi.org/10.1007/978-3-030-46140-9_4

Alatrista-Salas, Hugo ; Lavado, Pablo ; Morzan, Juandiego et al. / Detecting anomalies in time-varying media crime news using tensor decomposition. Information management and big data: 6th International Conference, SIMBig 2019, Proceedings. editor / Juan Antonio Lossio-Ventura ; Nelly Condori-Fernandez ; Jorge Carlos Valverde-Rebaza. Cham, 2020. pp. 35-45 (Communications in Computer and Information Science).

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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.",

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doi = "10.1007/978-3-030-46140-9_4",

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Alatrista-Salas, H, Lavado, P, Morzan, J, Nuñez-del-Prado, M & Yamada, G 2020, Detecting anomalies in time-varying media crime news using tensor decomposition. in JA Lossio-Ventura, N Condori-Fernandez & JC Valverde-Rebaza (eds), Information management and big data: 6th International Conference, SIMBig 2019, Proceedings. Communications in Computer and Information Science, vol. 1070 CCIS, Cham, pp. 35-45, Communications in Computer and Information Science, 1/01/20. https://doi.org/10.1007/978-3-030-46140-9_4

Detecting anomalies in time-varying media crime news using tensor decomposition. / Alatrista-Salas, Hugo; Lavado, Pablo; Morzan, Juandiego et al.
Information management and big data: 6th International Conference, SIMBig 2019, Proceedings. ed. / Juan Antonio Lossio-Ventura; Nelly Condori-Fernandez; Jorge Carlos Valverde-Rebaza. Cham, 2020. p. 35-45 (Communications in Computer and Information Science; Vol. 1070 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Alatrista-Salas, Hugo

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AU - Yamada, Gustavo

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020/4/23

Y1 - 2020/4/23

N2 - 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.

AB - 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.

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Alatrista-Salas H, Lavado P, Morzan J, Nuñez-del-Prado M, Yamada G. Detecting anomalies in time-varying media crime news using tensor decomposition. In Lossio-Ventura JA, Condori-Fernandez N, Valverde-Rebaza JC, editors, Information management and big data: 6th International Conference, SIMBig 2019, Proceedings. Cham. 2020. p. 35-45. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-46140-9_4

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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