CrisisMMD

Multimodal twitter datasets from natural disasters

Firoj Alam, Ferda Ofli, Muhammad Imran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multimedia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this online information, if processed timely and effectively, is extremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multimodal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.

Original languageEnglish
Title of host publication12th International AAAI Conference on Web and Social Media, ICWSM 2018
PublisherAAAI press
Pages465-473
Number of pages9
ISBN (Electronic)9781577357988
Publication statusPublished - 1 Jan 2018
Event12th International AAAI Conference on Web and Social Media, ICWSM 2018 - Palo Alto, United States
Duration: 25 Jun 201828 Jun 2018

Other

Other12th International AAAI Conference on Web and Social Media, ICWSM 2018
CountryUnited States
CityPalo Alto
Period25/6/1828/6/18

Fingerprint

Disasters

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Alam, F., Ofli, F., & Imran, M. (2018). CrisisMMD: Multimodal twitter datasets from natural disasters. In 12th International AAAI Conference on Web and Social Media, ICWSM 2018 (pp. 465-473). AAAI press.

CrisisMMD : Multimodal twitter datasets from natural disasters. / Alam, Firoj; Ofli, Ferda; Imran, Muhammad.

12th International AAAI Conference on Web and Social Media, ICWSM 2018. AAAI press, 2018. p. 465-473.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Alam, F, Ofli, F & Imran, M 2018, CrisisMMD: Multimodal twitter datasets from natural disasters. in 12th International AAAI Conference on Web and Social Media, ICWSM 2018. AAAI press, pp. 465-473, 12th International AAAI Conference on Web and Social Media, ICWSM 2018, Palo Alto, United States, 25/6/18.
Alam F, Ofli F, Imran M. CrisisMMD: Multimodal twitter datasets from natural disasters. In 12th International AAAI Conference on Web and Social Media, ICWSM 2018. AAAI press. 2018. p. 465-473
Alam, Firoj ; Ofli, Ferda ; Imran, Muhammad. / CrisisMMD : Multimodal twitter datasets from natural disasters. 12th International AAAI Conference on Web and Social Media, ICWSM 2018. AAAI press, 2018. pp. 465-473
@inproceedings{20e90896ba114d128212027195970f32,
title = "CrisisMMD: Multimodal twitter datasets from natural disasters",
abstract = "During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multimedia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this online information, if processed timely and effectively, is extremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multimodal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.",
author = "Firoj Alam and Ferda Ofli and Muhammad Imran",
year = "2018",
month = "1",
day = "1",
language = "English",
pages = "465--473",
booktitle = "12th International AAAI Conference on Web and Social Media, ICWSM 2018",
publisher = "AAAI press",

}

TY - GEN

T1 - CrisisMMD

T2 - Multimodal twitter datasets from natural disasters

AU - Alam, Firoj

AU - Ofli, Ferda

AU - Imran, Muhammad

PY - 2018/1/1

Y1 - 2018/1/1

N2 - During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multimedia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this online information, if processed timely and effectively, is extremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multimodal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.

AB - During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multimedia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this online information, if processed timely and effectively, is extremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multimodal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.

UR - http://www.scopus.com/inward/record.url?scp=85050637466&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050637466&partnerID=8YFLogxK

M3 - Conference contribution

SP - 465

EP - 473

BT - 12th International AAAI Conference on Web and Social Media, ICWSM 2018

PB - AAAI press

ER -