survey

Processing Social Media Messages in Mass Emergency: A Survey

Authors:

Muhammad Imran,

Carlos Castillo,

Sarah ViewegAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 47, Issue 4

Article No.: 67, Pages 1 - 38

https://doi.org/10.1145/2771588

Published: 26 June 2015 Publication History

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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Index Terms

Processing Social Media Messages in Mass Emergency: A Survey
1. Information systems
  1. Information systems applications

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 47, Issue 4

July 2015

573 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2775083

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering/University of Florida/Gainesville, FL

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Publication History

Published: 26 June 2015

Accepted: 01 April 2015

Revised: 01 April 2015

Received: 01 July 2014

Published in CSUR Volume 47, Issue 4

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https://doi.org/10.1109/TCSS.2024.3453226
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https://doi.org/10.1016/j.osnem.2024.100299
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https://doi.org/10.1016/j.jjimei.2024.100314
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