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

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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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Authors: Imran, M., Castillo, C., Diaz, F., & Vieweg, S.
Affiliations: Qatar Computing Research Institute, Doha, Qatar
Microsoft Research, New York NY
ORCID: Imran, Muhammad
Castillo, Carlos
Vieweg, Sarah
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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Processing Social Media Messages in Mass Emergency: A Survey

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