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AIDR: artificial intelligence for disaster response

Authors:

Sarah ViewegAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 159 - 162

https://doi.org/10.1145/2567948.2577034

Published: 07 April 2014 Publication History

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Abstract

We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters into a set of user-defined categories of information (e.g., "needs", "damage", etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/.

References

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Cited By

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Li DQu ZLyu CZhang LZuo W(2024)Fuzzy SVM With Mahalanobis Distance for Situational Awareness-Based Recognition of Public Health EmergenciesInternational Journal of Fuzzy System Applications10.4018/IJFSA.34211713:1(1-21)Online publication date: 18-Apr-2024
https://doi.org/10.4018/IJFSA.342117
Chandra RLakshmi D(2024)Application of Artificial Intelligence in Disaster Management and Their ChallengesInternet of Things and AI for Natural Disaster Management and Prediction10.4018/979-8-3693-4284-8.ch001(1-27)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-4284-8.ch001
Decoupes RRoche MTeisseire M(2024)GeoNLPlifyIntelligent Data Analysis10.3233/IDA-23004028:2(507-531)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/IDA-230040
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Index Terms

AIDR: artificial intelligence for disaster response
1. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Software implementation planning
        Software design techniques
    2. Software development process management

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Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Demonstration

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all

Li DQu ZLyu CZhang LZuo W(2024)Fuzzy SVM With Mahalanobis Distance for Situational Awareness-Based Recognition of Public Health EmergenciesInternational Journal of Fuzzy System Applications10.4018/IJFSA.34211713:1(1-21)Online publication date: 18-Apr-2024
https://doi.org/10.4018/IJFSA.342117
Chandra RLakshmi D(2024)Application of Artificial Intelligence in Disaster Management and Their ChallengesInternet of Things and AI for Natural Disaster Management and Prediction10.4018/979-8-3693-4284-8.ch001(1-27)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-4284-8.ch001
Decoupes RRoche MTeisseire M(2024)GeoNLPlifyIntelligent Data Analysis10.3233/IDA-23004028:2(507-531)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/IDA-230040
Nguyen TRudra KChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Human vs ChatGPT: Effect of Data Annotation in Interpretable Crisis-Related Microblog ClassificationProceedings of the ACM Web Conference 202410.1145/3589334.3648141(4534-4543)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648141
Pavani TMalla S(2024)A review of deep learning techniques for disaster management in social media: trends and challengesThe European Physical Journal Special Topics10.1140/epjs/s11734-024-01172-9Online publication date: 3-May-2024
https://doi.org/10.1140/epjs/s11734-024-01172-9
Kumar RSinha RSaha SJatowt A(2024)Extracting the Full Story: A Multimodal Approach and Dataset to Crisis Summarization in TweetsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.343669011:6(7846-7856)Online publication date: Dec-2024
https://doi.org/10.1109/TCSS.2024.3436690
Garg PChakraborty RDandapat S(2024)OntoDSumm: Ontology-Based Tweet Summarization for Disaster EventsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326602511:2(2724-2739)Online publication date: Apr-2024
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Alam FAlam TOfli FImran M(2024)Robust Training of Social Media Image Classification ModelsIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.323083911:1(546-565)Online publication date: Feb-2024
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Bansal DGrover RSaini NSaha S(2024) GenSumm : A Joint Framework for Multi-Task Tweet Classification and Summarization Using Sentiment Analysis and Generative Modelling IEEE Transactions on Affective Computing10.1109/TAFFC.2021.313151615:4(1838-1855)Online publication date: Oct-2024
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Shingate MGumaste D(2024)Improving Ensemble Classifier for Natural Language Situational Information Analysis2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)10.1109/INNOCOMP63224.2024.00057(305-311)Online publication date: 25-May-2024
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