Detecting reports of mass emergency events on twitter

V. Pekar; J. Binner; H. Najafi; C. Hale; Vincent A. Schmidt

Detecting reports of mass emergency events on twitter

V. Pekar, J. Binner, H. Najafi, C. Hale, Vincent A. Schmidt

Finance

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

Abstract

The paper addresses the problem of detecting eyewitness reports of mass emergencies on Twitter. This is the first work to conduct a large-scale comparative evaluation of classification features extracted from Twitter posts, using different learning algorithms and datasets representing a broad range of mass emergencies including both natural and technological disasters. We investigate the relative importance of different feature types as well as on the effect of several feature selection methods applied to this problem. Because the task of detecting mass emergencies is characterized by high heterogeneity of the data, our primary focus is on identifying those features that are capable of separating mass emergency reports from other messages, irrespective of the type of the disaster.

Original language	English
Title of host publication	Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016
Editors	Hamid R. Arabnia, David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica, Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M.G. Solo, Fernando G. Tinetti
Publisher	CSREA Press
Pages	137-143
Number of pages	7
ISBN (Electronic)	1601324383, 9781601324382
Publication status	Published - 2016
Event	2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016 - Las Vegas, United States Duration: 25 Jul 2016 → 28 Jul 2016

Publication series

Name	Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016

Conference

Conference	2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016
Country/Territory	United States
City	Las Vegas
Period	25/07/16 → 28/07/16

Bibliographical note

Publisher Copyright:
CSREA Press ©.

Keywords

Disaster management
Machine learning
Risk assessment
Social media analysis
Text classification

ASJC Scopus subject areas

Software
Artificial Intelligence

Cite this

Pekar, V., Binner, J., Najafi, H., Hale, C., & Schmidt, V. A. (2016). Detecting reports of mass emergency events on twitter. In H. R. Arabnia, D. de la Fuente, R. Dziegiel, E. B. Kozerenko, P. M. LaMonica, R. A. Liuzzi, J. A. Olivas, T. Waskiewicz, G. Jandieri, A. M. G. Solo, & F. G. Tinetti (Eds.), Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016 (pp. 137-143). (Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016). CSREA Press.

Pekar, V. ; Binner, J. ; Najafi, H. et al. / Detecting reports of mass emergency events on twitter. Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016. editor / Hamid R. Arabnia ; David de la Fuente ; Roger Dziegiel ; Elena B. Kozerenko ; Peter M. LaMonica ; Raymond A. Liuzzi ; Jose A. Olivas ; Todd Waskiewicz ; George Jandieri ; Ashu M.G. Solo ; Fernando G. Tinetti. CSREA Press, 2016. pp. 137-143 (Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016).

@inproceedings{8672e8b5ff6a49babe96e56c3e2dc200,

title = "Detecting reports of mass emergency events on twitter",

abstract = "The paper addresses the problem of detecting eyewitness reports of mass emergencies on Twitter. This is the first work to conduct a large-scale comparative evaluation of classification features extracted from Twitter posts, using different learning algorithms and datasets representing a broad range of mass emergencies including both natural and technological disasters. We investigate the relative importance of different feature types as well as on the effect of several feature selection methods applied to this problem. Because the task of detecting mass emergencies is characterized by high heterogeneity of the data, our primary focus is on identifying those features that are capable of separating mass emergency reports from other messages, irrespective of the type of the disaster.",

keywords = "Disaster management, Machine learning, Risk assessment, Social media analysis, Text classification",

author = "V. Pekar and J. Binner and H. Najafi and C. Hale and Schmidt, {Vincent A.}",

note = "Publisher Copyright: CSREA Press {\textcopyright}.; 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016 ; Conference date: 25-07-2016 Through 28-07-2016",

year = "2016",

language = "English",

series = "Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016",

publisher = "CSREA Press",

pages = "137--143",

editor = "Arabnia, {Hamid R.} and {de la Fuente}, David and Roger Dziegiel and Kozerenko, {Elena B.} and LaMonica, {Peter M.} and Liuzzi, {Raymond A.} and Olivas, {Jose A.} and Todd Waskiewicz and George Jandieri and Solo, {Ashu M.G.} and Tinetti, {Fernando G.}",

booktitle = "Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016",

}

Pekar, V, Binner, J, Najafi, H, Hale, C & Schmidt, VA 2016, Detecting reports of mass emergency events on twitter. in HR Arabnia, D de la Fuente, R Dziegiel, EB Kozerenko, PM LaMonica, RA Liuzzi, JA Olivas, T Waskiewicz, G Jandieri, AMG Solo & FG Tinetti (eds), Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016. Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016, CSREA Press, pp. 137-143, 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016, Las Vegas, United States, 25/07/16.

Detecting reports of mass emergency events on twitter. / Pekar, V.; Binner, J.; Najafi, H. et al.
Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016. ed. / Hamid R. Arabnia; David de la Fuente; Roger Dziegiel; Elena B. Kozerenko; Peter M. LaMonica; Raymond A. Liuzzi; Jose A. Olivas; Todd Waskiewicz; George Jandieri; Ashu M.G. Solo; Fernando G. Tinetti. CSREA Press, 2016. p. 137-143 (Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016).

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

TY - GEN

T1 - Detecting reports of mass emergency events on twitter

AU - Pekar, V.

AU - Binner, J.

AU - Najafi, H.

AU - Hale, C.

AU - Schmidt, Vincent A.

N1 - Publisher Copyright: CSREA Press ©.

PY - 2016

Y1 - 2016

N2 - The paper addresses the problem of detecting eyewitness reports of mass emergencies on Twitter. This is the first work to conduct a large-scale comparative evaluation of classification features extracted from Twitter posts, using different learning algorithms and datasets representing a broad range of mass emergencies including both natural and technological disasters. We investigate the relative importance of different feature types as well as on the effect of several feature selection methods applied to this problem. Because the task of detecting mass emergencies is characterized by high heterogeneity of the data, our primary focus is on identifying those features that are capable of separating mass emergency reports from other messages, irrespective of the type of the disaster.

AB - The paper addresses the problem of detecting eyewitness reports of mass emergencies on Twitter. This is the first work to conduct a large-scale comparative evaluation of classification features extracted from Twitter posts, using different learning algorithms and datasets representing a broad range of mass emergencies including both natural and technological disasters. We investigate the relative importance of different feature types as well as on the effect of several feature selection methods applied to this problem. Because the task of detecting mass emergencies is characterized by high heterogeneity of the data, our primary focus is on identifying those features that are capable of separating mass emergency reports from other messages, irrespective of the type of the disaster.

KW - Disaster management

KW - Machine learning

KW - Risk assessment

KW - Social media analysis

KW - Text classification

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M3 - Conference contribution

AN - SCOPUS:85068347439

T3 - Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016

SP - 137

EP - 143

BT - Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016

A2 - Arabnia, Hamid R.

A2 - de la Fuente, David

A2 - Dziegiel, Roger

A2 - Kozerenko, Elena B.

A2 - LaMonica, Peter M.

A2 - Liuzzi, Raymond A.

A2 - Olivas, Jose A.

A2 - Waskiewicz, Todd

A2 - Jandieri, George

A2 - Solo, Ashu M.G.

A2 - Tinetti, Fernando G.

PB - CSREA Press

T2 - 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016

Y2 - 25 July 2016 through 28 July 2016

ER -

Pekar V, Binner J, Najafi H, Hale C, Schmidt VA. Detecting reports of mass emergency events on twitter. In Arabnia HR, de la Fuente D, Dziegiel R, Kozerenko EB, LaMonica PM, Liuzzi RA, Olivas JA, Waskiewicz T, Jandieri G, Solo AMG, Tinetti FG, editors, Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016. CSREA Press. 2016. p. 137-143. (Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016).

Detecting reports of mass emergency events on twitter

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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