Clustering of Social Media Messages for Humanitarian Aid Response during Crisis

Abstract

Social media has quickly grown into an essential tool for people to communicate and express their needs during crisis events. Prior work in analyzing social media data for crisis management has focused primarily on automatically identifying actionable (or, informative) crisis-related messages. In this work, we show that recent advances in Deep Learning and Natural Language Processing outperform prior approaches for the task of classifying informativeness and encourage the field to adopt them for their research or even deployment. We also extend these methods to two sub-tasks of informativeness and find that the Deep Learning methods are effective here as well.

6 pages, 1 figure. Research work was done while Swati was interning at Dataminr Inc. and presented at the AI for Social Good, Harvard CRCS Workshop 2020 (https://aiforgood2020.github.io)

References in corpus (2)

• Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks
• Semi-supervised Discovery of Informative Tweets During the Emerging Disasters