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Twicalli: An Earthquake Detection System Based on Citizen Sensors Used for Emergency Response in Chile

Published: 07 August 2017 Publication History

Abstract

In this talk I will describe "Twicalli", a real-time earthquake detection system based on citizen sensors. This system is publicly available for over a year, at http://twicalli.cl, and is currently in use as a decision support tool by the National Seismology Office and by the Hydrographic and the Oceanographic Service in Chile. The novelty of our system relies on the fact that it has a very good precision and recall tradeoff for earthquakes of all magnitude ranges that were reported on Twitter. Our earthquake detection methodology is simple, efficient, unsupervised, and it can detect earthquakes reported globally in any language and any region. This complements existing approaches that are either: i) supervised and customized to a particular geographical region, which makes them very expensive to scale geographically and keep up-to-date, or ii) unsupervised with low earthquake recall.The evaluation of our system, performed during a 9-month period, shows that our solution is competitive to the best state-of-the-art methods, providing very good precision and recall performance for a wide range of earthquake magnitudes.

Cited By

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  • (2022)Cross-platform analysis of public responses to the 2019 Ridgecrest earthquake sequence on Twitter and RedditScientific Reports10.1038/s41598-022-05359-912:1Online publication date: 31-Jan-2022
  • (2020)An Analysis of Twitter Responses to the 2019 Ridgecrest Earthquake Sequence2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00127(810-818)Online publication date: Dec-2020

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cover image ACM Conferences
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
August 2017
1476 pages
ISBN:9781450350228
DOI:10.1145/3077136
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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New York, NY, United States

Publication History

Published: 07 August 2017

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Author Tags

  1. data visualization
  2. decision support
  3. emergency management
  4. event detection
  5. social media
  6. social sensing
  7. twitter

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SIGIR '17
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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2022)Cross-platform analysis of public responses to the 2019 Ridgecrest earthquake sequence on Twitter and RedditScientific Reports10.1038/s41598-022-05359-912:1Online publication date: 31-Jan-2022
  • (2020)An Analysis of Twitter Responses to the 2019 Ridgecrest Earthquake Sequence2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00127(810-818)Online publication date: Dec-2020

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