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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Information Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings
Editores	Juan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
Editorial	Springer
Páginas	35-45
Número de páginas	11
ISBN (versión impresa)	9783030461393
DOI	https://doi.org/10.1007/978-3-030-46140-9_4
Estado	Publicada - 2020
Publicado de forma externa	Sí
Evento	6th International Conference on Information Management and Big Data, SIMBig 2019 - Lima, Perú Duración: 21 ago. 2019 → 23 ago. 2019

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1070 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	6th International Conference on Information Management and Big Data, SIMBig 2019
País/Territorio	Perú
Ciudad	Lima
Período	21/08/19 → 23/08/19

ODS de las Naciones Unidas

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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. En 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",

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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. En 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, Perú, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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

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AN - SCOPUS:85084795913

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

A2 - Lossio-Ventura, Juan Antonio

A2 - Condori-Fernandez, Nelly

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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. En Lossio-Ventura JA, Condori-Fernandez N, Valverde-Rebaza JC, editores, 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

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