Award Abstract # 1802284
BIGDATA: IA: Collaborative Research: Domain Adaptation Approaches for Classifying Crisis Related Data on Social Media

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: KANSAS STATE UNIVERSITY
Initial Amendment Date: December 1, 2017
Latest Amendment Date: December 1, 2017
Award Number: 1802284
Award Instrument: Standard Grant
Program Manager: Aidong Zhang
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 20, 2017
End Date: January 31, 2019 (Estimated)
Total Intended Award Amount: $400,000.00
Total Awarded Amount to Date: $400,000.00
Funds Obligated to Date: FY 2017 = $4,499.00
History of Investigator:
  • Cornelia Caragea (Principal Investigator)
    cornelia@uic.edu
Recipient Sponsored Research Office: Kansas State University
1601 VATTIER STREET
MANHATTAN
KS  US  66506-2504
(785)532-6804
Sponsor Congressional District: 01
Primary Place of Performance: Kansas State University
1601 Vattier Street
Manhattan
KS  US  66506-1100
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): CFMMM5JM7HJ9
Parent UEI:
NSF Program(s): Big Data Science &Engineering
Primary Program Source: 01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7433, 8083
Program Element Code(s): 808300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The project investigates the use of big-data analysis techniques for classifying crisis-related data in social media with respect to situational awareness categories, such as caution, advice, fatality, injury, and support, with the goal of helping emergency response teams identify useful information. A major challenge is the scale of the data, where millions of short messages are continuously posted during a disaster, and need to be analyzed. The use of current technologies based on automated machine learning is limited due to the lack of labeled data for an emergent target disaster, and the fact that every event is unique in terms of geography, culture, infrastructure, technology, and the people involved. To tackle the above challenges, domain adaptation techniques that make use of existing labeled data from prior disasters and unlabeled data from a current disaster are designed. The resulting models are continuously updated and improved based on feedback from crowdsourcing volunteers. The research will provide real, usable solutions to emergency response organizations and will enable these organizations to improve the speed, quality and efficiency of their response.

The research provides novel solutions based on domain adaptation and deep neural networks to tackle the unique challenges in applying machine learning for crisis-related data analysis, specifically the volume and velocity challenges of big crisis data. Domain adaptation approaches enable the transfer of information from prior source disasters to an emergenet target disaster. Deep learning approaches make it possible to employ large amounts of labeled source data and unlabeled target data, and to incrementally update the models as more labeled target data becomes available. Large-scale analysis across combinations of source and target crises will help identify patterns of transferable situational awareness knowledge. The resulting technical and social solutions will be blended together for use in data management and emergency response.

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