CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1145/2771588

Cites

170 of 170 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 174 references

4 of the 174 references couldn’t be matched to a known paper.

Research works printed but not linked: 16

  1. N. S. Baron . 2003. Language of the internet . In The Stanford handbook for language engineers , A. Farghali (Ed.). CSLI publications, Stanford, CA , 59--127. N. S. Baron. 2003. Language of the internet. In The Stanford handbook for language engineers, A. Farghali (Ed.). CSLI publications, Stanford, CA, 59--127.
  2. Andy Carvin. 2013. Distant Witness. CUNY Journalism Press. Andy Carvin. 2013. Distant Witness. CUNY Journalism Press.
  3. 10.5555/2601921 · DOI
  4. 10.5555/1218883.1218887 · DOI
  5. Isabelle Guyon and André Elisseeff . 2003 . Special issue on variable and feature selection . Journal of Machine Learning Research, 3. Isabelle Guyon and André Elisseeff. 2003. Special issue on variable and feature selection. Journal of Machine Learning Research, 3.
  6. Lewis M. Killian . 2002. Methods for disaster research: Unique or not? In Methods of Disaster Research , Robert A. Stallings (Ed.) . Xlibris , Philadelphia, PA , 49--93. Lewis M. Killian. 2002. Methods for disaster research: Unique or not? In Methods of Disaster Research, Robert A. Stallings (Ed.). Xlibris, Philadelphia, PA, 49--93.
  7. Kirill Kireyev , Leysia Palen , and K. Anderson . 2009. Applications of topics models to analysis of disaster-related Twitter data . In NIPS Workshop on Applications for Topic Models: Text and Beyond , Vol. 1 . Kirill Kireyev, Leysia Palen, and K. Anderson. 2009. Applications of topics models to analysis of disaster-related Twitter data. In NIPS Workshop on Applications for Topic Models: Text and Beyond, Vol. 1.
  8. Kalev Leetaru and Philip A . Schrodt . 2013 . GDELT : Global database of events, language, and tone. In Proc. of ISA. http://gdelt.utdallas.edu/. Kalev Leetaru and Philip A. Schrodt. 2013. GDELT: Global database of events, language, and tone. In Proc. of ISA. http://gdelt.utdallas.edu/.
  9. Andrew Kachites McCallum . 2002 . Mallet: A machine learning for language toolkit. Andrew Kachites McCallum. 2002. Mallet: A machine learning for language toolkit.
  10. Richard McCreadie , M.- Dyaa Albakour , Stuart Mackie , Nut Limosopathan , Craig Macdonald , Iadh Ounis , and B. Taner Dinçer . 2013. Experiments with terrier in contextual suggestion, temporal summarisation and web tracks . In Proc. of TREC. Richard McCreadie, M.-Dyaa Albakour, Stuart Mackie, Nut Limosopathan, Craig Macdonald, Iadh Ounis, and B. Taner Dinçer. 2013. Experiments with terrier in contextual suggestion, temporal summarisation and web tracks. In Proc. of TREC.
  11. A. Robinson , Alexander Savelyev , Scott Pezanowski , and Alan M . MacEachren . 2013 b. Understanding the utility of geospatial information in social media. In Proc. ISCRAM. 918--922. A. Robinson, Alexander Savelyev, Scott Pezanowski, and Alan M. MacEachren. 2013b. Understanding the utility of geospatial information in social media. In Proc. ISCRAM. 918--922.
  12. 10.5555/1571651 · DOI
  13. Robert A. Stallings . 2002. Methods for disaster research: Unique or not? In Methods of Disaster Research , Robert A. Stallings (Ed.) . Xlibris , Philadelphia, PA , 21--44. Robert A. Stallings. 2002. Methods for disaster research: Unique or not? In Methods of Disaster Research, Robert A. Stallings (Ed.). Xlibris, Philadelphia, PA, 21--44.
  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.
  15. Ingmar Weber and Yelena Mejova . 2013 . Twitter and the Real World. CIKM’13 tutorial . Retrieved May 11, 2015 from https://sites.google.com/site/twitterandtherealworld/home. Ingmar Weber and Yelena Mejova. 2013. Twitter and the Real World. CIKM’13 tutorial. Retrieved May 11, 2015 from https://sites.google.com/site/twitterandtherealworld/home.
  16. Tan Xu , Paul McNamee , and Douglas W . Oard . 2013 . HLTCOE temporal summarization. In Proc. of TREC. Tan Xu, Paul McNamee, and Douglas W. Oard. 2013. HLTCOE temporal summarization. In Proc. of TREC.

Cited by

Sorted by citations

Items 201–300 of 883

Related

See all

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review