Open AccessProceedings Article
Using social media to enhance emergency situation awareness
Jie Yin,Sarvnaz Karimi,Andrew Lampert,Mark Cameron,Bella Robinson,Robert Power +5 more
- pp 4234-4238
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TLDR
The described system uses natural language processing and data mining techniques to extract situation awareness information from Twitter messages generated during various disasters and crises.Abstract:
Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.read more
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A Deep Learning Approach for Tweet Classification and Rescue Scheduling for Effective Disaster Management
TL;DR: A deep learning model combining attention based Bi-directional Long Short-Term Memory and Convolutional Neural Network to classify the tweets and an adaptive multi-task hybrid scheduling algorithm considering resource constraints to perform an effective rescue scheduling operation considering different rescue priorities are developed.
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Mining Social Media for Newsgathering: A Review
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STIMULATE: A System for Real-time Information Acquisition and Learning for Disaster Management
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A comparative analysis for spatio-temporal spreading patterns of emergency news.
TL;DR: A systematic, comprehensive evaluation framework is built and applied to 81 million reposts from Sina Weibo and finds that the spreading of emergency news generally exhibits a shorter life cycle, a shorter active period, and fewer fluctuations in the aftermath of the peak than other types of news, while propagation is limited to a few steps from the source.
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Social media analytics and internet of things: survey
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References
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Proceedings ArticleDOI
Earthquake shakes Twitter users: real-time event detection by social sensors
TL;DR: This paper investigates the real-time interaction of events such as earthquakes in Twitter and proposes an algorithm to monitor tweets and to detect a target event and produces a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location.
Proceedings ArticleDOI
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Journal ArticleDOI
Bursty and Hierarchical Structure in Streams
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Proceedings ArticleDOI
You are where you tweet: a content-based approach to geo-locating twitter users
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Proceedings ArticleDOI
Fast and effective text mining using linear-time document clustering
Bjornar Larsen,Chinatsu Aone +1 more
TL;DR: An unsupervised, near-linear time text clustering system that offers a number of algorithm choices for each phase, and a refinement to center adjustment, “vector average damping,” that further improves cluster quality.