Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks

TL;DR

This paper presents a neural network approach for rapid classification of crisis-related social media data, outperforming existing methods especially when labeled data is scarce during the initial hours of a disaster.

Contribution

The study introduces neural network models that eliminate the need for feature engineering and effectively utilize out-of-event data for early crisis classification.

Findings

Neural networks outperform state-of-the-art methods in crisis tweet classification.

Models perform well with limited or no labeled data during early disaster hours.

Out-of-event data enhances early classification accuracy.

Abstract

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.