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Cornelia Caragea, Adrian Silvescu, & Andrea Tapia. (2016). Identifying Informative Messages in Disasters using Convolutional Neural Networks. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Social media is a vital source of information during any major event, especially natural disasters. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, with the exponential increase in the volume of social media data, so comes the increase in data that are irrelevant to a disaster, thus, diminishing peoples? ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. In this paper, we present an approach to identifying informative messages in social media streams during disaster events. Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the ?bag of words? and n-grams as features on several datasets of messages from flooding events.
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Grégoire Burel, & Harith Alani. (2018). Crisis Event Extraction Service (CREES) – Automatic Detection and Classification of Crisis-related Content on Social Media. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 597–608). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media posts tend to provide valuable reports during crises. However, this information can be hidden in large amounts of unrelated documents. Providing tools that automatically identify relevant posts, event types (e.g., hurricane, floods, etc.) and information categories (e.g., reports on affected individuals, donations and volunteering, etc.) in social media posts is vital for their efficient handling and consumption. We introduce the Crisis Event Extraction Service (CREES), an open-source web API that automatically classifies posts during crisis situations. The API provides annotations for crisis-related documents, event types and information categories through an easily deployable and accessible web API that can be integrated into multiple platform and tools. The annotation service is backed by Convolutional Neural Networks (CNNs) and validated against traditional machine learning models. Results show that the CNN-based API results can be relied upon when dealing with specific crises with the benefits associated with the usage word embeddings.
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Jens Kersten, Anna Kruspe, Matti Wiegmann, & Friederike Klan. (2019). Robust filtering of crisis-related tweets. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Social media enables fast information exchange and status reporting during crises. Filtering is usually required to
identify the small fraction of social media stream data related to events. Since deep learning has recently shown to
be a reliable approach for filtering and analyzing Twitter messages, a Convolutional Neural Network is examined for
filtering crisis-related tweets in this work. The goal is to understand how to obtain accurate and robust filtering
models and how model accuracies tend to behave in case of new events. In contrast to other works, the application
to real data streams is also investigated. Motivated by the observation that machine learning model accuracies
highly depend on the used data, a new comprehensive and balanced compilation of existing data sets is proposed.
Experimental results with this data set provide valuable insights. Preliminary results from filtering a data stream
recorded during hurricane Florence in September 2018 confirm our results.
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