The Role of Social Media and Artificial Intelligence for Disaster Response
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Keynote slides for ISCRAM 2016. "Social Media platforms such as Twitter are invaluable sources of time-critical information. Information on social media communicated during emergencies convey timely and actionable information. For rapid crisis response, real-time insights are important for emergency responders. Although, many humanitarian organizations would like to use this information, however they struggle due a number of issues such as information overload, information vagueness, less credible and misinformation. In this talk, I will describe the role of social media and potential artificial intelligence computational techniques useful for humanitarian organizations and decision makers to make sense of social media data for rapid crisis response."
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The Role of Social Media and Artificial Intelligence for Disaster Response
- 2. This Talk is About… • The Role of Informa2on in Time-cri2cal Situa2ons – Natural disasters and their destruc+ons – Man-made disasters and mass convergence events • The Role of Social Media for Disaster Response – Par+cular focus on micro-blogging plaJorms – Availability of various types of informa+on and opportuni+es • The Role of Ar2ficial Intelligence for Disaster Response – How AI is useful in disaster response – Various AI techniques, approaches, and tools – Work of crisis compu+ng group at QCRI – Ongoing research – Future direc+ons
- 7. - Between 2008 and 2014, 184 million people displaced by natural disasters - Over 60 million due to conflicts - An average 26.4 million each year The Urgency to Act and Plan
- 20. Twi<er Crisis-Related Data (All) - Twioer data from 13 recent crises - Over 100,000 tweets - Informa2on types - Types of sources Source: Qatar Compu+ng Research Ins+tute - Published in World Humanitarian Data and Trends 2014 (UN OCHA)
- 28. MERS Outbreak Twi<er Data Analysis Middle East Respiratory Syndrome (MERS) Twioer data collec+on from: 2014-04-27 to 2014-07-14 using hashtag #MERS (Total = 215,370) Data analysis: Reports of symptoms Affected people reports Death reports Disease transmission reports Preven+on ques+ons Treatment ques+ons Reports of signs or symptoms such as fever, cough or ques+ons Reports of affected people due to the MERS disease Reports of deaths due to the MERS disease Ques+ons or sugges+ons related to the preven+on of disease Reports or ques+ons related to the transmission of the disease Ques+ons or sugges+ons regarding the treatment of the disease
- 34. 2013 Pakistan Earthquake September 28 at 07:34 UTC 2010 Hai2 Earthquake January 12 at 21:53 UTC Data and Opportuni2es Social Media Plamorms Availability of Immense Data: Around 16 thousands tweets per minute were posted during the hurricane Sandy in the US. Opportuni2es: - Early warning and event detec2on - Situa2onal awareness - Ac2onable informa2on - Rapid crisis response - Post-disaster analysis Disease outbreaks
- 37. Data Characteris2cs and Prepara2on • Single-word slangs: pls (please), srsly (seriously) • Mul2-word slangs: imo (in my opinion) • Misspellings: missin (missing), ovrcme (overcome) • Phone2c subs2tu2on: 2morrow (tomorrow) • Word without spaces: prayfornepal (pray for nepal) Can you guess? “r u ok m8” ?? >> “Are you OK, mate?”
- 44. Data collec+on 1 2 Human annota+ons Machine training 3 Classifica+on 4 ONLINE APPROACH DATA COLLECTION H A Learning-1 CLASSIFICATION OF DATA & DECISION MAKING PROCESS Learning-2 Learning-3 … Learning-n Human annota+on - 1 Human annota+on - 2 Human annota+on - 3 … Human annota+on - n First few hours Informa2on Processing (Real-2me)
- 45. Big Challenges – 4Vs • Volume Scale of data (20m tweets in 5 days Typhoon Oklahoma) • Velocity Analysis of streaming data (16k/min during Sandy) • Variety Different forms/types of data (informa+on types) • Veracity Uncertainty of data
- 47. Crowdsourced Stream Processing Combining human and machine computa2on Difficult, ambiguous items to be labeled by crowd Automatic processing Automatic processing output output Performing verification Providing training data a: Split automatic/manual processing b: Detect-verify paradigm Automatic processing Automatic processing output c: Improving quality through active learning input input input Difficult, ambiguous items to be labeled by crowd Automatic processing Automatic processing output Performing verification Providing training data a: Split automatic/manual processing b: Detect-verify paradigm Automatic processing Automatic processing output c: Improving quality through active learning input input input Difficult, ambiguous items to be labeled by crowd Automatic processing Automatic processing output output Performing verification Providing training data a: Split automatic/manual processing b: Detect-verify paradigm Automatic processing Automatic processing output c: Improving quality through active learning input input input Quality assurance loops: human processing elements do the work, automa+c processing elements check for consistency Process-verify: work is done automa+cally, humans check low-confidence or borderline cases Online supervised learning: humans train machines to perform work automa+cally
- 48. hop://aidr.qcri.org/ AIDR —Ar+ficial Intelligence for Disaster Response— is a free, open-source, and easy-to-use plaJorm to automa+cally filter and classify relevant tweets posted during humanitarian crises. 1 2 3 Collect Curate Classify Awarded the Grand Prize in the Open Source Soaware World Challenge 2015
- 49. AIDR: From End-users Perspec2ve Collec2on Classifier(s) • Keywords, hashtags • Geographical bounding box • Languages • Follow specific set of users A collec2on is a set of filters A classifier is a set of tags • Dona2ons requests & offers • Damage & causali2es • Eyewitness accounts • … 2 steps approach 1 2 hop://aidr.qcri.org/
- 57. High-level Architecture hop://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
- 59. Quality vs. Cost in AIDR hop://aidr.qcri.org/ Goal: Maximizing quality while minimizing cost • Quality • classifica+on accuracy • Precision • Cost (human labels) • monetary in case of paid-workers • +me in case of volunteers
- 63. UNICEF U-Report and AIDR-SMS AI to Answer Heath Queries via SMS • Every hour Zambian youth get infected with HIV/AIDS • UNICEF launched U-Report project in Zambia • Usage of U-Report plaJorm has recently increased 300%
- 67. Media Coverage
- 69. Human Annota2on - Challenges 1- Labeling task selec2on • Which tasks to pick for labeling? • No duplicate tasks should be labeled • Priori2ze tasks that are likely to increase classifier’s accuracy Crowdsourcing is a big research topic. We address two challenges here:
- 70. Twi<er Crises Datasets 1. Joplin-2011 • Consists of 206,764 tweets collected using (#joplin) 2. Sandy-2012 • Consists of 4,906,521 tweets collected using (#sandy, hurricane sandy, …) 3. Oklahoma-2013 • Consists of 2,742,588 tweets collected using (Oklahoma, tornado, …)
- 72. Labeling Task Selec2on Experiment: Is de-duplica2on necessary? Phase Train Phase Test AUC (without de- duplica2on) AUC (with de- duplica2on) S1 (pre) 1,500 S1 (pre) 500 0.78 0.74 S1 (pre) 500 S1 (pre) 500 0.73 0.72 S2 (impact) 500 S2 (impact) 500 0.80 0.72 S3 (post) 500 S3 (post) 500 0.79 0.73 S4 (post’) 500 S4 (post’) 500 0.70 0.64 • 29-74% of tweets are re-tweets & 60-75% are near duplicates • Duplica+on causes an ar2ficial increase in accuracy • Necessary to reduce classifier bias. Otherwise learning on a fewer concepts • Necessary to improve workers experience [Rogstadius et al. 2011]
- 73. Labeling Task Selec2on Experiment: Passive learning vs. Ac2ve learning JOPLIN SANDY OKLAHOMA S1 S2 S3 S4 AUC stabilize with fewer training items using ac+ve learning
- 74. Labeling Task Scheduling • All-at-once labeling • Obtain 1,500 labels on S1 and use all for training • Cumula2ve labeling • Obtain 500 labels in each of S1, S2, and S3 and train on labels available up to each phase • Independent labeling • Obtain 500 labels in each of S1, S2, and S3 and use the most recent labels for training, discarding old. 2- Labeling task scheduling
- 78. Same Domain Learning Training data Machine learning model Tes+ng data infer predict Apples Apples Apples Oranges Different shapes, colors, skins, tastes, etc. Source domain Target domain Oranges Oranges BUT
- 80. Domain Adapta2on
- 81. Model Adapta2on Experiments • Model adapta+on using single source – Using both: in-domain and cross-domain • Model adapta+on using mul2ple sources – In-domain – Mul+ple source events without the target – Mul+ple source events with the target • Model adapta+on in special cases – Same languages – Similar languages
- 82. Observa2ons & Findings • Data from early hours of a crisis help • Past events of same type are useful • Same language data as target event is also useful • Similar languages are also useful • Cross-domain training does not show significant improvements
- 84. Image Processing for Damage Assessment Tasks • Image categoriza2on • E.g. building, bridge, road damage • Damage and severity assessment • Given a damage image, iden+fy severity of damage (low, mild, high)
- 85. Deep Learning to Improve Classifica2on Tasks • Improve text classifica2on performance • Availability of big data • Automa+c features learning • Binary and mul+-class classifica+on • Tes+ng data from mul+ple past events
- 86. Transfer Learning Differences in classifica2on tasks: • Different classifica+on tasks • Different types of disasters, stakeholders, informa+on needs Task: • Learn from source to classify target • Seman+c similarity between tasks • Instances similarity between domains • Instance weigh+ng
- 87. Summariza2on and Priori2za2on of Ac2onable Informa2on Informa2on needs & problem: • Different stakeholders • Different goals, requirements, and info. needs General situa2onal awareness vs. Target situa2onal awareness • High-level general updates from an event • Specific updates (infrastructure damages)
- 89. Towards Standard Baselines and Datasets CrisisNLP.qcri.org - Access to 52 million tweets - Around 50k labeled tweets into humanitarian categories - Largest word2vec embeddings trained on 52m crisis-related tweets - Out-of-vocabulary dic2onaries
- 91. Upcoming Book
- 93. ACM Compu2ng Survey Processing Social Media Messages in Mass Emergency: A Survey [Imran et al. 2015]
- 94. Conclusions • Informa2on bestows power for disaster response – People need informa+on as much as water, shelter, and food – Disasters are unavoidable, but planning can lessen their effects • Social media as 2me-cri2cal informa2on source – Early warnings, event detec+on, event monitoring – Availability of informa+on opens new opportuni+es • Ar2ficial Intelligence for Disaster Response – Applied research at its best – AI + humans-in-the-loop can enable rapid crisis response – AI techniques useful for: • Situa+onal awareness • Ac+onable informa+on extrac+on • Summariza+on