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Robust Domain Adaptation Approach for Tweet Classification for Crisis Response

  • Reem ALRashdiEmail author
  • Simon O’Keefe
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)

Abstract

Information posted by people on Twitter during crises can significantly improve crisis response towards reducing human and financial loss. Deep learning algorithms can identify related tweets to reduce information overloaded which prevents humanitarian organizations from using Twitter posts. However, they heavily rely on labeled data which is unavailable for emerging crises. And because each crisis has its own features such as location, occurring time and social media response, current models are known to suffer from generalizing to unseen disaster events when pretrained on past ones. To solve this problem, we propose a domain adaptation approach that makes use of a distant supervision-based framework to label the unlabeled data from emerging crises. Then, pseudo-labeled target data, along with labeled-data from similar past disasters, are used to build the target model. Our results show that our approach can be seen as a general robust method to classify unseen tweets from current events.

Keywords

Domain adaptation Twitter data Crisis response Distant supervision 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of YorkYorkUK
  2. 2.CSSE DepartmentUniversity of HailHailSaudi Arabia

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