Data Mining and Machine Learning LabData Mining and Machine Learning Lab
Beyond Crowdsourcing for HADR
Huan Liu, Shamanth Kumar and Huiji Gao
Data Mining and Machine Learning LabData Mining and Machine Learning Lab
Outline
‒Motivation
–Crowdsourcing for Disaster Relief
–Inadequacies of Current Crowdsourcing Systems
–Our Methodology
–Demonstration
Data Mining and Machine Learning LabData Mining and Machine Learning Lab
Motivation
•Catastrophic Disasters:
Haiti earthquake/cholera.
Middle–east revolutions.
Japanese tsunami and earthquake.
•Social media for disaster relief:
Revolutionize the role of media
Information disseminator
Communication tool
Data Mining and Machine Learning LabData Mining and Machine Learning Lab
•Crowdsourcing leverages participatory social mediaservices and tools to collect information
•Crowdsourcing allows capable crowds to participate invarious HADR tasks.
•Crowdsourcing integrated with crisis map has become apowerful tool in humanitarian assistance and disasterrelief (HADR).
Social Media for Crowdsourcing
Data Mining and Machine Learning LabData Mining and Machine Learning Lab
Applications of Social Media & Crowdsourcing for Disaster Relief
•Uses for Individuals
–Find missing people
–Early warning of disasters
–Get information on relief work progress
–Find location of shelters & medical resources
–Get in touch with officials and relief workers (more ways to ask forhelp)
•Uses for Agencies
–Get situational awareness first hand from citizen reporters
–Coordination platform
–Send updates on progress of relief work
–Discredit rumors
–Obtain public feedback
Data Mining and Machine Learning LabData Mining and Machine Learning Lab
Inadequacies of Current Crowdsourcing Systems
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Data Mining and Machine Learning LabData Mining and Machine Learning Lab
How We Can Help
•Building crowdsourcing systems to aid in event analysis
–Automate data collection & data storage for event analysis
–Preprocessing and summarize collected data for quick interpretation
–Visualize crowdsourced data
•Building a coordination system for better collaboration
–Coordination mechanism designed for disaster relief
–Intelligent crisis map view to facilitate the response
–Enhancing communications among agencies
Data Mining and Machine Learning LabData Mining and Machine Learning Lab
Our tools
ACT BlogTrackers TweetTrackers
Crowdsourced information
Feedback information source
Situational awareness
Post event analysis
Crowdsourced information
Situational awareness
Near real time informationaggregation
Post event analysis
Crowdsourced information
Groupsourced information
Multi-layer requests view
Inter agency coordination
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ACT (ASU Coordination Tracker)
Four Modules
•Request Collection
-Crowdsourcing
-Groupsourcing
•Response
•Coordination
•Statistics
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BlogTrackers
Three modules
•Data Collection
•Crowdsourcing
•Analysis Module
•Visualization
Data Mining and Machine Learning LabData Mining and Machine Learning Lab
TweetTrackers
Three modules
•Data Collection
–Crowdsourcing
•Analysis
•Visualization
Data Mining and Machine Learning LabData Mining and Machine Learning Lab
Acknowledgments
•DMML members, in particular, Geoff Barbier,Fred Morstatter, and Patrick Mcinerney.
• This work benefits from the ONR’s vision onSocial Computing, Digital Revolution, andHA/DR.
Office of Naval Research Office of Naval Research