Introduction to Machine Learning: An Application to Disaster Response
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![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](https://image.slidesharecdn.com/mlaidrqstp-151118060041-lva1-app6891/75/introduction-to-machine-learning-an-application-to-disaster-response-22-2048.jpg?cb=1668996620)
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. Introduction to Machine Learning: An Application to Disaster Response Muhammad Imran & Shafiq Joty Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar
- 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. @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. @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. @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. @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. 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. 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. 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. 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. TIME-CRITICAL ANLYSIS OF BIG CRISIS DATA Apply machine learningApply crowdsourcing
- 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. AIDR – COLLECTION SETUP Collection detail dashboard http://aidr.qcri.org/ Geographical region filterLanguage filter Collection definition
- 24. http://aidr.qcri.org/ AIDR – CLASSIFIER SETUP
- 25. AIDR – CLASSIFIER SETUP (cont.) http://aidr.qcri.org/
- 26. AIDR – CROWDSOURCING-1 Internal Tagging Interface http://aidr.qcri.org/
- 27. AIDR – CROWDSOURCING-2 MicroMapper Interface (browser clicker) http://aidr.qcri.org/ Mobile clicker
- 28. AIDR – OUTPUT http://aidr.qcri.org/ Training examples Classified output (achieved accuracy ~ 75%)
- 29. - Killed 27 people - A million evacuated - $114 million of damage TYPHOON HAGUPIT (2014)
- 31. AIDR has been awarded the Grand Prize in the Open Source Software World Challenge 2015
- 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!
Editor's Notes
- Finding tactical and actionable information from a millions of messages that people post on social media is a complex and challenging task. For this purpose, specifically for disasters we came up with a sensible ontology that has mainly three stages. Every stage refine a piece of information that thus can highly contribute to disaster management. In order to get to the actionable information it is required that we first categories a coming message to a predefined category that is of disaster-specific.
- I would like to start with showing you the results of our last year paper in ISCRAM. These charts show that a significant amount of valuable information is available can be extracted from tweets. In this work, we used state-of-art supervised machine learning techniques to classify tweets posted during disaster situations. Our conclusion from that work was Social media platforms like Twitter contain useful information. Now the big question is how one can get that useful information during an on-going disaster for an effective disaster response?
- One of the big challenges in the crowdsourcing
- AIDR tagger is a machine computational component. Users define classifiers by specifying categories they like tweets to classified against. AIDR tagger requires tagged examples for its learning process.
- On the left side, screenshot list training examples of a particular collection. One can review, and remove if required. On the right side, screenshot shows the classified output. That is, tweets classified into categories with confidence score.
- During disasters, nothing better than helping those who are affected to save lives. That’s exactly what we did during the Typhoon Hagupit that struck the Philippines in early December 2014 on a request from UN to help them find requests of help/needs, infrastructure damage, aid needs and provided using Social Media data. On your left side, the guardian page covering the whole story, and on your right side a map generated by our platforms AIDR, MicroMappers.
- One of the big challenges in the crowdsourcing