Clustering of Social Media Messages for Humanitarian Aid Response during Crisis
Wright State University（莱特州立大学）；Dataminr Inc.（Dataminr公司）
Swati Padhee, Tanay Kumar Saha, Joel Tetreault, Alejandro Jaimes
备注：
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)
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.
Indian Institute of Information Technology Raichur（印度信息技术学院赖布尔分校）；National Institute of Technology, Rourkela（鲁尔克拉国家理工学院）；Space Research Institute, Austrian Academy of Sciences（奥地利科学院空间研究所）；Nikhef（荷兰国家亚原子物理研究所）
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Rochester Institute of Technology（罗切斯特理工学院）；Cognitive Insight Limited（认知洞察有限公司）；Daffodil International University（达福德国际大学）
