Processing social media messages in Mass Emergency: A survey

Muhammad Imran, Carlos Castillo, Fernando Diaz, Sarah Vieweg

Research output: Contribution to journalArticle

315 Citations (Scopus)

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 mediamessages 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.

Original languageEnglish
Article number67
JournalACM Computing Surveys
Volume47
Issue number4
DOIs
Publication statusPublished - 1 Jun 2015

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Keywords

  • Crisis computing
  • Disastermanagement
  • Mass emergencies
  • Socialmedia

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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