Artificial Intelligence for Disaster Response
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AIDR is a free open-source platform that uses machine learning and crowdsourcing to automatically filter and classify relevant tweets during humanitarian crises. It collects tweets based on keywords, hashtags, location, and followed users. Classifiers then tag tweets with categories like donations, damage reports, or eyewitness accounts. The platform achieves around 75% accuracy in classification by training models on tagged tweets and leveraging random forest algorithms.
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Artificial Intelligence for Disaster Response
- 1. http://aidr.qcri.org/ Muhammad Imran, Carlos Castillo, Ji Lucas, Patrick Meier, Sarah Vieweg Qatar Computing Research Institute (QCRI) Doha, Qatar AIDR: Artificial Intelligence for Disaster Response
- 2. http://aidr.qcri.org/ 2013 Pakistan Earthquake: September 28 at 07:34 UTC 2010 Haiti Earthquake: January 12 at 21:53 UTC CRISIS COMPUTING … Content generator Social Media Messages
- 3. CRISIS COMPUTING http://aidr.qcri.org/ Apply machine learningApply crowdsourcing
- 4. CRISIS COMPUTING: CHALLENGES http://aidr.qcri.org/ - High velocity (e.g., 16k tweets / minute during Sandy) - Redundant information (high volume of re-tweets) - Unstructured messages - Short text (140 characters in case of tweets) - Multilingual messages - Poor grammar
- 5. 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.
- 6. ML+ CROWDSOURCING http://aidr.qcri.org/ AIDR = Machine learning + Crowdsourcing
- 7. AIDR: FROM END-USERS PERSPECTIVE Collection Classifier(s) • Keywords, Hashtags • Geographical bounding box • Language • 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/
- 8. AIDR APPROACH Collection Classifier(s) Tag Tag Tag Tag Learner Classifier-1 Tag Tag Tag Tag 30k/min Classifier-2 http://aidr.qcri.org/
- 9. AIDR – COLLECTION SETUP Collection detail dashboard http://aidr.qcri.org/ Geographical region filterLanguage filter Collection definition
- 10. http://aidr.qcri.org/ AIDR – CLASSIFIER SETUP
- 11. AIDR – CLASSIFIER SETUP (cont.) http://aidr.qcri.org/
- 12. AIDR – CROWDSOURCING-1 Internal Tagging Interface http://aidr.qcri.org/
- 13. AIDR – CROWDSOURCING-2 MicroMapper Interface (browser clicker) http://aidr.qcri.org/ Mobile clicker
- 14. AIDR – OUTPUT http://aidr.qcri.org/ Training examples Classified output (achieved accuracy ~ 75%)
- 15. AIDR – CLASSIFICATION USING RANDOM FOREST http://aidr.qcri.org/ T1 T2 T3 Tn Features Category a Category b Data: predictors with known response Goal: predict the response when it’s unknown (e.g., Hepatitis) Protein < 45.43 V = yesno Protein >= 26 X <= p
- 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. Thank you!
Hinweis der Redaktion
- During situations like mass emergencies, disasters, epidemics nothing better than Social Media platforms like Twitter which provides unique opportunities for both affected people and Emergency responders. People share situational awareness messages, and ask for help, donations, food, water, shelter etc. On the other hand responders want to help.
- The challenge is to identify relevant and actionable content in near real-time in this growing stack of Big Crisis Data, however, it is like looking for the proverbial needle in a giant haystack. Possible solutions: for example If you employ only human computation you end up having filter failure problem, and if you only use machine learning, you have to go offline, label examples, train and then classify.
- 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.
- 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.