Crisis Event Extraction Service (CREES) - Automatic Detection and Classification of Crisis-related Content on Social Media

Burel, Gregoire and Alani, Harith (2018). Crisis Event Extraction Service (CREES) - Automatic Detection and Classification of Crisis-related Content on Social Media. In: 15th International Conference on Information Systems for Crisis Response and Management, 20-23 May 2018, Rochester, NY, USA.

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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