Data Mining and Machine Learning Lab
Lessons Learned in Using Social Media forDisaster Relief
- ASU Crisis Response Game
Mohammad Ali Abbasi, Shamanth Kumar, Huan LiuComputer Science and Engineering, Arizona State University
Jose Augusto Andrade Filho Department of Computer Science-ICMC, University of Sao Paulo
Data Mining and Machine Learning Lab
Social Media, Disaster Relief, Game
• People used Social Media to send their report or requests during disasters or emergencies– Haiti earthquake, Hurricane Irene, Tsunami in Japan
• Twitter was one of the first sources of eyewitness information during the Mumbai terror attacks in 2008
• Many HA/DR agencies are planning to use social media in disaster recovery efforts
• To test Social Media for Disaster Relief effort in a simulated controlled environment we designed a ASU Crisis Response Game
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Data Mining and Machine Learning Lab
Social Media, Disaster Relief, Game
• Social Media in disasters or crises– Haiti earthquake (2010)– Hurricane Irene (2011)– Tsunami in Japan (2011)
• We designed ASU Crisis Response Game to:
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Create a training capability to understand
the effective use of social media in crisis
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Game Scenario
• A disaster or an emergency happened• People use social media to report
damages or ask for help• NGO
– Collect data from social media – Filter and rank requests– Create missions– Send First responders to help victims
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ASU Crisis Response Game
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ASU Crisis Response Game
• 75 Volunteers played for 4 hours• 25 Teams of victims• 8 Teams of First-responders• 20 People in Command center• 7 Different locations in ASU campus
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Game Exercise and Lessons Learned
• Collecting Tweets and Short Messages– Two redundant systems
• Filtering system (Manual or Automatic?)– Clustering, Spam detection, Ranking
• Educating people– People don’t know how to use social media during crisis
• GeoLocation information– Usually not included
• Language– Text analysis tools usually can handle English
Data Mining and Machine Learning Lab
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Thanks!
Mohammad-Ali Abbasi
Acknowledgments:
This research was sponsored in part by the Office of Naval Research
We would also like to thank
• Members of DMML Lab and all volunteers from ASU
• Our collaborators Catherine Graham from Humanity Road Inc.
• Mark Bradshaw and his team from NSWCDD, Office of Joint Staff
• Dr. Rebecca Goolsby from ONR
• Professor Kathleen Carley and her team from CMU
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