Public Riots in Twitter: Domain-Based Event Filtering During Civil Unrest

Arturo Oncevay; Marco Sobrevilla; Hugo Alatrista-Salas; Andrés Melgar

doi:10.1007/978-3-030-55814-7_4

Public Riots in Twitter: Domain-Based Event Filtering During Civil Unrest

Arturo Oncevay, Marco Sobrevilla, Hugo Alatrista-Salas, Andrés Melgar

Universidad del Pacífico

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

1 Cita (Scopus)

Resumen

Civil unrest is public manifestations, where people demonstrate their position for different causes. Sometimes, violent events or riots are unleashed in this kind of events, and these can be revealed from tweets posted by involved people. This study describes a methodology to detect riots within the time of a protest to identify potential adverse developments from tweets. Using two own datasets related to a violent and non-violent protest in Peru, we applied temporal clustering to obtain events and identify a tweet headline per cluster. We then extracted relevant terms for the scoring and ranking process using a different domain and contrast corpus built from different sources. Finally, we filtered the relevant events for the violence domain by using a contrast evaluation between the two datasets. The obtained results highlight the adequacy of the proposed approach.

Idioma original	Inglés
Título de la publicación alojada	ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium - International Workshops
Subtítulo de la publicación alojada	DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, Proceedings
Editores	Ladjel Bellatreche, Mária Bieliková, Omar Boussaïd, Jérôme Darmont, Barbara Catania, Elena Demidova, Fabien Duchateau, Mark Hall, Tanja Mercun, Maja Žumer, Boris Novikov, Christos Papatheodorou, Thomas Risse, Oscar Romero, Lucile Sautot, Guilaine Talens, Robert Wrembel
Páginas	49-59
Número de páginas	11
ISBN (versión digital)	9783030558130
DOI	https://doi.org/10.1007/978-3-030-55814-7_4
Estado	Publicada - 1 ene. 2020
Evento	Communications in Computer and Information Science - Duración: 1 ene. 2020 → …

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1260 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	Communications in Computer and Information Science
Período	1/01/20 → …

Nota bibliográfica

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Palabras clave

Clustering
Event detection
Riot
Social media analysis

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1007/978-3-030-55814-7_4

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Oncevay, A., Sobrevilla, M., Alatrista-Salas, H., & Melgar, A. (2020). Public Riots in Twitter: Domain-Based Event Filtering During Civil Unrest. En L. Bellatreche, M. Bieliková, O. Boussaïd, J. Darmont, B. Catania, E. Demidova, F. Duchateau, M. Hall, T. Mercun, M. Žumer, B. Novikov, C. Papatheodorou, T. Risse, O. Romero, L. Sautot, G. Talens, & R. Wrembel (Eds.), ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium - International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, Proceedings (pp. 49-59). (Communications in Computer and Information Science; Vol. 1260 CCIS). https://doi.org/10.1007/978-3-030-55814-7_4

Oncevay, Arturo ; Sobrevilla, Marco ; Alatrista-Salas, Hugo et al. / Public Riots in Twitter : Domain-Based Event Filtering During Civil Unrest. ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium - International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, Proceedings. editor / Ladjel Bellatreche ; Mária Bieliková ; Omar Boussaïd ; Jérôme Darmont ; Barbara Catania ; Elena Demidova ; Fabien Duchateau ; Mark Hall ; Tanja Mercun ; Maja Žumer ; Boris Novikov ; Christos Papatheodorou ; Thomas Risse ; Oscar Romero ; Lucile Sautot ; Guilaine Talens ; Robert Wrembel. 2020. pp. 49-59 (Communications in Computer and Information Science).

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title = "Public Riots in Twitter: Domain-Based Event Filtering During Civil Unrest",

abstract = "Civil unrest is public manifestations, where people demonstrate their position for different causes. Sometimes, violent events or riots are unleashed in this kind of events, and these can be revealed from tweets posted by involved people. This study describes a methodology to detect riots within the time of a protest to identify potential adverse developments from tweets. Using two own datasets related to a violent and non-violent protest in Peru, we applied temporal clustering to obtain events and identify a tweet headline per cluster. We then extracted relevant terms for the scoring and ranking process using a different domain and contrast corpus built from different sources. Finally, we filtered the relevant events for the violence domain by using a contrast evaluation between the two datasets. The obtained results highlight the adequacy of the proposed approach.",

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author = "Arturo Oncevay and Marco Sobrevilla and Hugo Alatrista-Salas and Andr{\'e}s Melgar",

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Oncevay, A, Sobrevilla, M, Alatrista-Salas, H & Melgar, A 2020, Public Riots in Twitter: Domain-Based Event Filtering During Civil Unrest. En L Bellatreche, M Bieliková, O Boussaïd, J Darmont, B Catania, E Demidova, F Duchateau, M Hall, T Mercun, M Žumer, B Novikov, C Papatheodorou, T Risse, O Romero, L Sautot, G Talens & R Wrembel (eds.), ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium - International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, Proceedings. Communications in Computer and Information Science, vol. 1260 CCIS, pp. 49-59, Communications in Computer and Information Science, 1/01/20. https://doi.org/10.1007/978-3-030-55814-7_4

Public Riots in Twitter: Domain-Based Event Filtering During Civil Unrest. / Oncevay, Arturo; Sobrevilla, Marco; Alatrista-Salas, Hugo et al.
ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium - International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, Proceedings. ed. / Ladjel Bellatreche; Mária Bieliková; Omar Boussaïd; Jérôme Darmont; Barbara Catania; Elena Demidova; Fabien Duchateau; Mark Hall; Tanja Mercun; Maja Žumer; Boris Novikov; Christos Papatheodorou; Thomas Risse; Oscar Romero; Lucile Sautot; Guilaine Talens; Robert Wrembel. 2020. p. 49-59 (Communications in Computer and Information Science; Vol. 1260 CCIS).

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

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T1 - Public Riots in Twitter

T2 - Communications in Computer and Information Science

AU - Oncevay, Arturo

AU - Sobrevilla, Marco

AU - Alatrista-Salas, Hugo

AU - Melgar, Andrés

PY - 2020/1/1

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N2 - Civil unrest is public manifestations, where people demonstrate their position for different causes. Sometimes, violent events or riots are unleashed in this kind of events, and these can be revealed from tweets posted by involved people. This study describes a methodology to detect riots within the time of a protest to identify potential adverse developments from tweets. Using two own datasets related to a violent and non-violent protest in Peru, we applied temporal clustering to obtain events and identify a tweet headline per cluster. We then extracted relevant terms for the scoring and ranking process using a different domain and contrast corpus built from different sources. Finally, we filtered the relevant events for the violence domain by using a contrast evaluation between the two datasets. The obtained results highlight the adequacy of the proposed approach.

AB - Civil unrest is public manifestations, where people demonstrate their position for different causes. Sometimes, violent events or riots are unleashed in this kind of events, and these can be revealed from tweets posted by involved people. This study describes a methodology to detect riots within the time of a protest to identify potential adverse developments from tweets. Using two own datasets related to a violent and non-violent protest in Peru, we applied temporal clustering to obtain events and identify a tweet headline per cluster. We then extracted relevant terms for the scoring and ranking process using a different domain and contrast corpus built from different sources. Finally, we filtered the relevant events for the violence domain by using a contrast evaluation between the two datasets. The obtained results highlight the adequacy of the proposed approach.

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A2 - Duchateau, Fabien

A2 - Hall, Mark

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Oncevay A, Sobrevilla M, Alatrista-Salas H, Melgar A. Public Riots in Twitter: Domain-Based Event Filtering During Civil Unrest. En Bellatreche L, Bieliková M, Boussaïd O, Darmont J, Catania B, Demidova E, Duchateau F, Hall M, Mercun T, Žumer M, Novikov B, Papatheodorou C, Risse T, Romero O, Sautot L, Talens G, Wrembel R, editores, ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium - International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, Proceedings. 2020. p. 49-59. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-55814-7_4

Public Riots in Twitter: Domain-Based Event Filtering During Civil Unrest

Resumen

Serie de la publicación

Conferencia

Nota bibliográfica

Palabras clave

ODS de las Naciones Unidas

Acceder al documento

Otros archivos y enlaces

Huella

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