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Cited by 883Public Relations and Crisis CommunicationDisaster Management and ResilienceText and Document Classification Technologies

The DOI matches its Crossref record; 170 of the 174 references it cites were found in a registry or an index.

Processing Social Media Messages in Mass Emergency: A Survey

Imran, Muhammad, Castillo, Carlos, Diaz, Fernando, Vieweg, Sarah·2015·ACM Computing Surveys
DOI 10.1145/2771588
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Abstract

Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions.

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Crossref · 10.1145/2771588

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Citations

Cites

Research works printed but not linked: 16

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  14. Muhammad Moeen Uddin , Muhammad Imran , and Hassan Sajjad . 2014 . Understanding types of users on Twitter . In Proc. of SocialCom. Muhammad Moeen Uddin, Muhammad Imran, and Hassan Sajjad. 2014. Understanding types of users on Twitter. In Proc. of SocialCom.
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Metadata

Authors
Imran, M., Castillo, C., Diaz, F., & Vieweg, S.
Affiliations
  • Qatar Computing Research Institute, Doha, Qatar
  • Microsoft Research, New York NY
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2015-06-26
Volume
47
Issue
4
Pages
1-38
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/2771588
OpenAlex
W1934362406
Cited by
804
References
174
Citations by year
2026: 96 · 2025: 60 · 2024: 43 · 2023: 51 · 2022: 61 · 2021: 72 · 2020: 90 · 2019: 101 · 2018: 91 · 2017: 73 · 2016: 54 · 2015: 11
FWCI
245.39
Type
journal article
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM Computing Surveys (publishedVersion)
Topics
  • Social Sciences › Communication › Public Relations and Crisis Communication
  • Social Sciences › Sociology and Political Science › Disaster Management and Resilience
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Keywords
social media, mass emergencies, disaster response, situational awareness, event detection, summarization, information extraction, information overload, message classification, emergency decision-making, emergency response coordination, natural hazards

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