Applications of Online Deep Learning for Crisis Response Using Social Media Information

Abstract

During natural or man-made disasters, humanitarian response organizations look for useful information to support their decision-making processes. Social media platforms such as Twitter have been considered as a vital source of useful information for disaster response and management. Despite advances in natural language processing techniques, processing short and informal Twitter messages is a challenging task. In this paper, we propose to use Deep Neural Network (DNN) to address two types of information needs of response organizations: 1) identifying informative tweets and 2) classifying them into topical classes. DNNs use distributed representation of words and learn the representation as well as higher level features automatically for the classification task. We propose a new online algorithm based on stochastic gradient descent to train DNNs in an online fashion during disaster situations. We test our models using a crisis-related real-world Twitter dataset.

Accepted at SWDM co-located with CIKM 2016. 6 pages, 2 figures. arXiv admin note: text overlap with arXiv:1608.03902

References in corpus (4)

• ADADELTA: An Adaptive Learning Rate Method
• Natural Language Processing (almost) from Scratch
• A Convolutional Neural Network for Modelling Sentences
• Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks

Cited by in corpus (5)

• CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding
• Deep Learning and Word Embeddings for Tweet Classification for Crisis Response
• CrisisViT: A Robust Vision Transformer for Crisis Image Classification
• Few-shot tweet detection in emerging disaster events
• Event-Related Bias Removal for Real-time Disaster Events