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Introduction to Machine Learning: An Application to Disaster Response

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Introduction to Machine Learning talk (part-2) focused on the applications of machine learning in the disaster response domain. In the first part of the talk, we presented different machine learning approaches.

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Introduction to Machine Learning: An Application to Disaster Response

  1. 1. Introduction to Machine Learning: An Application to Disaster Response Muhammad Imran & Shafiq Joty Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar
  2. 2. DISASTERS - SOCIAL MEDIA – RESPONSE EFFORTS Humans suffering from the impacts of disasters, crises, and armed conflicts. In the last two decades, 218 million people each year were affected by disasters; At an annual cost to the global economy that exceeds $300 billion. (Source: UN)
  3. 3. @NYGovCuomo orders closing of NYC bridges. Only Staten Island bridges unaffected at this time. Bridges must close by 7pm. #Sandy #NYC. rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours after they got separated from their mom when car submerged in si. #sandy #911buff freaking out. home alone. will just watch tv #Sandy #NYC. 400 Volunteers are needed for areas that #Sandy destroyed. SANDY HURRICANE TWEETS
  4. 4. @NYGovCuomo orders closing of NYC bridges. Only Staten Island bridges unaffected at this time. Bridges must close by 7pm. #Sandy #NYC. rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours after they got separated from their mom when car submerged in si. #sandy #911buff freaking out. home alone. will just watch tv #Sandy #NYC. 400 Volunteers are needed for areas that #Sandy destroyed. Personal Informative SANDY HURRICANE TWEETS
  5. 5. @NYGovCuomo orders closing of NYC bridges. Only Staten Island bridges unaffected at this time. Bridges must close by 7pm. #Sandy #NYC. rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours after they got separated from their mom when car submerged in si. #sandy #911buff freaking out. home alone. will just watch tv #Sandy #NYC. 400 Volunteers are needed for areas that #Sandy destroyed. Personal Informative Caution and Advice Reports of missing people Help/volunteers needed SANDY HURRICANE TWEETS
  6. 6. @NYGovCuomo orders closing of NYC bridges. Only Staten Island bridges unaffected at this time. Bridges must close by 7pm. #Sandy #NYC. rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours after they got separated from their mom when car submerged in si. #sandy #911buff freaking out. home alone. will just watch tv #Sandy #NYC. 400 Volunteers are needed for areas that #Sandy destroyed. Personal Informative Caution and Advice Reports of missing people Help/volunteers needed SANDY HURRICANE TWEETS
  7. 7. Personal Informative (Direct & Indirect) Other Caution and advice Casualties and damage Donations People missing, found, or seen Information source Siren heard, warning issued/lifted etc. People dead, injured, damage etc. Money, shelter, blood, goods, or services Webpages, photos, videos information sources … FINDING TACTICAL AND ACTIONABLE INFORMATION
  8. 8. USEFUL INFORMATION ON TWITTER Caution and advice Information source Donations Causalities & damage A siren heard Tornado warning issued/lifted Tornado sighting/touchdown 42% 50% 30% 12% 18% Photos as info. source Webpages info. source Videos as info. source 44% 20% 16% Other donations Money Equipment, shelter, Volunteers, Blood 38% 8% 54% People injured People dead Damage 44% 44% 2% 16% 10% % of informative tweets Ref: “Extracting Information Nuggets from Disaster-Related Messages in Social Media”. Imran et al. ISCRAM-2013, Baden-Baden, Germany.
  9. 9. INFORMATION PROCESSING PIPELINE (SUPERVISED LEARNING): OFFLINE APPROACH Data collection 1 2 Human annotations on sample data Machine training 3 Classification 4 Disaster Timeline: DATA COLLECTION
  10. 10. IMPACT AND RESPONSE TIMELINE Department of Community Safety, Queensland Govt. & UNOCHA, 2011 Disaster response (today) Disaster response (target) Target disaster response requires real-time processing of data.
  11. 11. TIME-CRITICAL ANLYSIS OF BIG CRISIS DATA Apply machine learningApply crowdsourcing
  12. 12. REQUIREMENS & CHALLENGES • Real-time analysis of data is required • For rapid crisis response • To reduce community harm • Combine human and machine intelligence • Usable and useful for end-users (mostly non-technical) • End-users (stakeholders) • Crisis managers (policy makers) • Crisis responders (field workers)
  13. 13. REQUIREMENS & CHALLENGES Other key challenges: • Volume Scale of data (20m tweets in 5 days) • Velocity Analysis of streaming data (16k/min) • Variety Different forms/types of data (information types) • Veracity Uncertainty of data
  14. 14. STREAM PROCESSING USING SUPERVISED ML Combining human and machine computation Quality assurance loops: human processing elements do the work, automatic processing elements check for consistency Process-verify: work is done automatically, humans check low-confidence or borderline cases Online supervised learning: humans train the machine to do the work automatically
  15. 15. Data collection 1 2 Human annotations Machine training 3 Classification 4 ONLINE APPROACH DATA COLLECTION H A Learning-1 CLASSIFICATION OF DATA & DECISION MAKING PROCESS Learning-2 Learning-3 … Learning-n Human annotation - 1 Human annotation - 2 Human annotation - 3 … Human annotation - n First few hours INFORMATION PROCESSING PIPELINE: ONLINE APPROACH (REAL-TIME)
  16. 16. http://aidr.qcri.org/ AIDR —Artificial Intelligence for Disaster Response— is a free, open-source, and easy-to-use platform to automatically filter and classify relevant tweets posted during humanitarian crises. 1 2 3 Collect Curate Classify
  17. 17. AIDR: FROM END-USERS PERSPECTIVE Collection Classifier(s) • Keywords, hashtags • Geographical bounding box • Languages • Follow specific set of users A collection is a set of filters A classifier is a set of tags • Donations requests & offers • Damage & causalities • Eyewitness accounts • … 2 step approach 1 2 http://aidr.qcri.org/
  18. 18. AIDR APPROACH Collection Classifier(s) Tag Tag Tag Tag Learner Classifier-1 Tag Tag Tag Tag 30k/min Classifier-2 http://aidr.qcri.org/
  19. 19. AIDR: HIGH-LEVEL ARCHTECTURE http://aidr.qcri.org/ Items Collector Feature Extractor Classifier(s) Learner Crowdsourcing Task GeneratorStream of incoming items from data sources Item & featuresItem An expert defines classifiers by giving a name and description for each category Expert Items Crowd workers/volunteers Model parameter Classified Item A list of classified items by category and classifier’s confidence Labeling tasks Labeled item Data source Data source
  20. 20. QUALITY VS. COST http://aidr.qcri.org/ • Gaining acceptable quality • Quality (classification accuracy) • Cost (human labels: monetary in case of paid-workers, time in case of volunteers) Quality vs. cost using passive learning Quality vs. cost using active learning
  21. 21. PERFORMANCE http://aidr.qcri.org/ • In terms of throughput and latency Throughput of feature extractor, classifier, and the system Latency of feature extractor, classifier, and the system
  22. 22. CHALLENGES: DOMAIN ADAPTATION http://aidr.qcri.org/ • Crisis-specific labels are necessary • Contrasting vocabulary use • Differences in public concerns, affected infrastructure • New labels should be collected for each new crisis [ Imran et al. 2013b ] • Domain adaptation • Train models using all past labeled data (all types of events) • Train on labeled data from past similar events • Train on data from neighboring countries on similar events
  23. 23. AIDR – COLLECTION SETUP Collection detail dashboard http://aidr.qcri.org/ Geographical region filterLanguage filter Collection definition
  24. 24. http://aidr.qcri.org/ AIDR – CLASSIFIER SETUP
  25. 25. AIDR – CLASSIFIER SETUP (cont.) http://aidr.qcri.org/
  26. 26. AIDR – CROWDSOURCING-1 Internal Tagging Interface http://aidr.qcri.org/
  27. 27. AIDR – CROWDSOURCING-2 MicroMapper Interface (browser clicker) http://aidr.qcri.org/ Mobile clicker
  28. 28. AIDR – OUTPUT http://aidr.qcri.org/ Training examples Classified output (achieved accuracy ~ 75%)
  29. 29. - Killed 27 people - A million evacuated - $114 million of damage TYPHOON HAGUPIT (2014)
  30. 30. DEMO http://aidr.qcri.org/
  31. 31. AIDR has been awarded the Grand Prize in the Open Source Software World Challenge 2015
  32. 32. http://aidr.qcri.org/ AIDR —Artificial Intelligence for Disaster Response— is a free, open-source, and easy-to-use platform to automatically filter and classify relevant tweets posted during humanitarian crises. Thank you!

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