Clustering of Social Media Messages for Humanitarian Aid Response during Crisis

• URL: http://arxiv.org/abs/2007.11756v1
• Date: Thu, 23 Jul 2020 02:18:05 GMT
• Title: Clustering of Social Media Messages for Humanitarian Aid Response during Crisis
• Authors: Swati Padhee (1), Tanay Kumar Saha (2), Joel Tetreault (2), and Alejandro Jaimes (2) ((1) Wright State University, Dayton, OH, (2) Dataminr Inc., New York, NY)
• Abstract summary: We show that recent advances in Deep Learning and Natural Language Processing outperform prior approaches for the task of classifying informativeness. We extend these methods to two sub-tasks of informativeness and find that the Deep Learning methods are effective here as well.
• Score: 47.187609203210705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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