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

Universidad del Pacífico

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 - 6th International Conference, SIMBig 2019, Proceedings
Editors	Juan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
Publisher	Springer
Pages	35-45
Number of pages	11
ISBN (Print)	9783030461393
DOIs	https://doi.org/10.1007/978-3-030-46140-9_4
State	Published - 2020
Externally published	Yes
Event	6th International Conference on Information Management and Big Data, SIMBig 2019 - Lima, Peru Duration: 21 Aug 2019 → 23 Aug 2019

Publication series

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

Conference

Conference	6th International Conference on Information Management and Big Data, SIMBig 2019
Country/Territory	Peru
City	Lima
Period	21/08/19 → 23/08/19

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). Springer. 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. Springer, 2020. pp. 35-45 (Communications in Computer and Information Science).

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title = "Detecting Anomalies in Time-Varying Media Crime News Using Tensor Decomposition",

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.",

keywords = "Crime, Event detection, Tensor decomposition",

author = "Hugo Alatrista-Salas and Pablo Lavado and Juandiego Morzan and Miguel Nu{\~n}ez-del-Prado and Gustavo Yamada",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 6th International Conference on Information Management and Big Data, SIMBig 2019 ; Conference date: 21-08-2019 Through 23-08-2019",

year = "2020",

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

language = "English",

isbn = "9783030461393",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "35--45",

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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, Springer, pp. 35-45, 6th International Conference on Information Management and Big Data, SIMBig 2019, Lima, Peru, 21/08/19. 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. Springer, 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

TY - GEN

T1 - Detecting Anomalies in Time-Varying Media Crime News Using Tensor Decomposition

AU - Alatrista-Salas, Hugo

AU - Lavado, Pablo

AU - Morzan, Juandiego

AU - Nuñez-del-Prado, Miguel

AU - Yamada, Gustavo

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

PY - 2020

Y1 - 2020

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.

KW - Crime

KW - Event detection

KW - Tensor decomposition

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U2 - 10.1007/978-3-030-46140-9_4

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

M3 - Conference contribution

AN - SCOPUS:85084795913

SN - 9783030461393

T3 - Communications in Computer and Information Science

SP - 35

EP - 45

BT - Information Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings

A2 - Lossio-Ventura, Juan Antonio

A2 - Condori-Fernandez, Nelly

A2 - Valverde-Rebaza, Jorge Carlos

PB - Springer

T2 - 6th International Conference on Information Management and Big Data, SIMBig 2019

Y2 - 21 August 2019 through 23 August 2019

ER -

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. Springer. 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

Keywords

UN SDGs

Access to Document

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