1. Data Mining and Machine Learning Lab Beyond Crowdsourcing for HADR Huan Liu, Shamanth Kumar and Huiji Gao

2. Data Mining and Machine Learning Lab Outline ‒ Motivation –Crowdsourcing for Disaster Relief –Inadequacies of Current Crowdsourcing Systems –Our Methodology –Demonstration

3. Data 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

4. Data Mining and Machine Learning Lab Crowdsourcing leverages participatory social media services and tools to collect information Crowdsourcing allows capable crowds to participate in various HADR tasks. Crowdsourcing integrated with crisis map has become a powerful tool in humanitarian assistance and disaster relief (HADR). Social Media for Crowdsourcing

5. Data 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 for help) 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

6. Data Mining and Machine Learning Lab Inadequacies of Current Crowdsourcing Systems Information is hidden in massive and noisy data –N–Numerous social media sources –U–Unfiltered information can be hard to interpret –T–Too many messages can be overwhelming for intelligent decision making Lack of a common coordination mechanism –D–Different focus and capabilities of HADR agencies –H–Hard to optimize resource allocation and distribution

7. Data 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

8. Data 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 information aggregation  Post event analysis  Crowdsourced information  Groupsourced information  Multi-layer requests view  Inter agency coordination

9. Data Mining and Machine Learning Lab ACT (ASU Coordination Tracker) Four Modules Request Collection -Crowdsourcing -Groupsourcing Response Coordination Statistics

10. Data Mining and Machine Learning Lab BlogTrackers Three modules Data Collection Crowdsourcing Analysis Module Visualization

11. Data Mining and Machine Learning Lab TweetTrackers Three modules Data Collection –Crowdsourcing Analysis Visualization

12. Data 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 on Social Computing, Digital Revolution, and HA/DR. Office of Naval Research Office of Naval Research