Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks
This addresses the challenge of rapid social media analysis during crises for emergency responders, though it is incremental as it applies existing neural networks to a specific domain.
The authors tackled the problem of classifying crisis-related tweets with limited labeled data by introducing neural network models that outperform state-of-the-art methods without feature engineering, achieving good results using out-of-event data in early disaster stages.
The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. The Scarcity of labeled data, particularly in the early hours of a crisis, delays the machine learning process. The current state-of-the-art classification methods require a significant amount of labeled data specific to a particular event for training plus a lot of feature engineering to achieve best results. In this work, we introduce neural network based classification methods for binary and multi-class tweet classification task. We show that neural network based models do not require any feature engineering and perform better than state-of-the-art methods. In the early hours of a disaster when no labeled data is available, our proposed method makes the best use of the out-of-event data and achieves good results.