A Hybrid Transfer Learning Approach to Migratable Disaster Assessment in Social Media Sensing

Yang Zhang, Ruohan Zong, Dong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social media sensing has emerged as a powerful sensing paradigm to collect the observations of the physical world by exploring the 'wisdom of crowd'. In this paper, we focus on a migratable disaster damage assessment problem in social media sensing applications. Our goal is to accurately identify the damage severity of affected areas in an unfolding disaster event using unlabeled social media data feeds (e.g., image posts on social media). Two fundamental challenges exist in solving our problem: i) different disaster events often have distinct characteristics (e.g., damage types, affected areas) that cannot be easily migrated; ii) it is non-Trivial to modify a damage assessment model from a previous event to adapt to a new event without using the labeled data from the new event. To address the above challenges, we develop SocialTrans, a hybrid deep transfer learning framework, to enable effective model migration for accurate damage assessment without using any training data from the studied disaster event. The evaluation results on four real-world disaster events show that SocialTrans consistently outperforms the state-of-The-Art baselines in accurately assessing the damage level of disasters.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
EditorsMartin Atzmuller, Michele Coscia, Rokia Missaoui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages131-138
Number of pages8
ISBN (Electronic)9781728110561
DOIs
StatePublished - Dec 7 2020
Externally publishedYes
Event12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands
Duration: Dec 7 2020Dec 10 2020

Publication series

NameProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Country/TerritoryNetherlands
CityVirtual, Online
Period12/7/2012/10/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Social Psychology
  • Communication

Fingerprint

Dive into the research topics of 'A Hybrid Transfer Learning Approach to Migratable Disaster Assessment in Social Media Sensing'. Together they form a unique fingerprint.

Cite this