Journal ArticleDOI
Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development
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TLDR
An earthquake reporting system for use in Japan is developed and an algorithm to monitor tweets and to detect a target event is proposed, which produces a probabilistic spatiotemporal model for the target event that can find the center of the event location.Abstract:
Twitter has received much attention recently. An important characteristic of Twitter is its real-time nature. We investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center of the event location. We regard each Twitter user as a sensor and apply particle filtering, which are widely used for location estimation. The particle filter works better than other comparable methods for estimating the locations of target events. As an application, we develop an earthquake reporting system for use in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (93 percent of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and notification is delivered much faster than JMA broadcast announcements.read more
Citations
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Scalable System for Opinion Mining on Twitter Data. Dynamic Visualization for Data Related to Refugees' Crisis and to Terrorist Attacks.
TL;DR: This paper presents a system created in order to process streamed data taken from Twitter and classify it into positive, negative or neutral, which can be visualized in a suggestive manner on Google Maps.
Proceedings Article
TriggerCit: Early Flood Alerting using Twitter and Geolocation - a comparison with alternative sources
Carlos Enrique Martí Bono,Barbara Pernici,Jose Luis Fernandez-Marquez,A. U. Ravi Shankar,Mehmet Ouguz Mulayim,Edoardo Milano,Universityof Geneva,Artificial Intelligence Research Institute IIIA-CSIC,United Nations Satellite Centre Unosat,United Nations Institute for Training,Research Unitar +10 more
TL;DR: In this article , an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation is proposed, which can support emergency response with evidence-based content posted by citizens and organisations during ongoing events.
Proceedings ArticleDOI
Spatiotemporal topic association detection on tweets
TL;DR: The concept of topic association and the associated mining algorithms are proposed and the concepts of participation ratio and participation index are used to measure the closeness among topics and proposed spatiotemporal index to calculate them efficiently.
Book ChapterDOI
Spatio-temporal decision support system for natural crisis management with TweetComP1
TL;DR: A qualitative end user evaluation is undertaken on the design of a social media crisis mapping platform for decision making in natural disasters where tweets are analysed to achieve situational awareness during earthquake and tsunami events.
Proceedings ArticleDOI
Machine Learning Approach for Sentiment Analysis in Crime Information Retrieval
TL;DR: In this article, the authors used machine learning algorithms such as Multinomal Naive Bayes, Random Forest Classifier, Linear SVM, and Nearestneighborhood (kNN) to find a better classifier.
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