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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Journal ArticleDOI
Opportunities and risks of disaster data from social media: a systematic review of incident information
Matti Wiegmann,Matti Wiegmann,Jens Kersten,Hansi Senaratne,Martin Potthast,Friederike Klan,Benno Stein +6 more
TL;DR: This work reviews 37 disaster and incident databases covering 27 incident types, compile a unified overview of the contained data and their collection processes, and identifies the missing or incomplete information.
Journal ArticleDOI
Big data would not lie: prediction of the 2016 Taiwan election via online heterogeneous information
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Journal ArticleDOI
Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events
TL;DR: In this paper, a hybrid approach that uses supervised learning model Support Vector Machine (SVM) combined with Bayes Factor Tree Augmented Naive Bayes (BFTAN) technique is proposed to accurately classify the input tweet while keeping in mind the different challenges of sentiment analysis.
Journal ArticleDOI
Uncovering Spatiotemporal Characteristics of Human Online Behaviors during Extreme Events
Chao Gao,Jiming Liu +1 more
TL;DR: This work identifies the distinct categories of human collective online concerns and durations based on the distributions of solo tweets and new incremental tweets about events and finds that there exists a self-similar evolution process for the collective responses within a region.
Proceedings Article
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