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Crisis Information Processing - with the power of A.I.


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Keynote delivered at the 10th International Conference on Social Informatics (SocInfo 2018), St.Petersburg, Russia, September 25–28, 2018.

Material is from the EU COMRADES project and includes work from all the members of COMRADES consortium. For more information on the project, please visit the website at

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Crisis Information Processing - with the power of A.I.

  1. 1. Crisis Information Processing with the power of A.I. Harith Alani Knowledge Media institute The Open University, UK @halani
  2. 2. AGEOF DISASTERS Kerala flooda, August 2018, ~400 fatalities, ~500K people displaced Attica, Greece, Wild fires, July 2018, 98 deaths Japan, Typhoon Jebi, Sept 2018, 17 deaths Hurricane Maria, Oct 2017, 3K fatalities Lombok, Indonesia, Earthquake, August 2018, over 500 deaths
  5. 5. POWER OF INFORMATION “Disaster-affected people need information as much as water, food, medicine or shelter. Information can save lives, livelihoods and resources.”
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  8. 8. DISASTER RESPONSE THROUGH SOCIAL MEDIA “The models that are emerging indicate that affected people are becoming extremely adept at using social media platforms in particular to engage in networked systems of response. This means they are able to post about specific needs and solicit individual responses to those needs, and that people offering specific help can also do so”
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  10. 10. FEMA MOBILE APP “Unfortunately, we’ve been underwhelmed with the use of that app, because I think everyone is at the Weather Channel doing their Instagrams there,” …. “Instead of trying to do everything ourselves, we need to find smarter ways to integrate the social media world more effectively into how we perform our business functions.” Scott Shoup chief data officer at FEMA
  11. 11. ”Immediate damage estimates based on FEMA models can miss areas of heavy impact. Augmenting initial models with real-time analysis of social media and crowdsourced information can help identify overlooked areas. Twitter-sourced estimates were virtually available as people tweeted distress signals, of these parcel-level damage estimates, 46 percent were not captured by FEMA estimates.” FEMA MISSES HURRICANE DAMAGE REPORTED ON TWITTER
  13. 13. WORKFLOW OF USHAHIDI & SIMILAR PLATFORMS citizen reporters digital responders Manual Annotations administrators Manual Verification Manual Publishing analysts/public/ research teams
  14. 14. SOCIAL MEDIA INFOSMOG DURING DISASTERS In the US, 1.1 million tweets were sent in the first day of Hurricane Sandy, and over 20 million in total ~800K photos with #Sandy hashtag on Instagram More than 23 million tweets were posted about the haze in Singapore In Nepal, more than half a million posts were shared about the devastating earthquake in 2015 >2.3M tweets were sent with the words “Haiti” or “Red Cross” in 2010 ~177 million tweets sent about the Japan 2011 earthquake disaster
  16. 16. REQUIREMENTS & CHALLENGES VOLUME VALUE VARIETY VALIDITY Too much content to handle manually More content is coming in all the time Rumours and hoaxes spread wild during disasters Content is often repetitive and uninformative Much of the content is irrelevant VELOCITY
  17. 17. Filtering out irrelevant information helps to tackle information overload How do we identify relevant and irrelevant information across diverse crises situations? Can we learn from one type of crisis situation, and apply it to another? Can we train our models on one language and apply it to another? RELEVANCY OF SOCIAL MEDIA POSTS
  18. 18. CRISES DATA
  19. 19. RELEVANCY
  20. 20. Query Filtering #hashtags, keywords disaster name disaster specific phrases locations filtered data FILTERING METHODS Post Collection text search topic modelling semantic search automatic categorisation filtered data supervisedunsupervised Event Label Machine Learning Classifiers (e.g., Naïve Bayes, SVM, J48, CNN) Features (e.g., n-grams, linguistic features, semantics)
  21. 21. 34 AUTOMATIC CLASSIFICATION N-grams Text length Count of nouns/verbs/pronouns Hashtags Mentions Readability score … Analysis Features
  22. 22. CLASSIFICATION MODELS HURRICANE HARVEY HURRICANE IRMA KERALA FLOODS LOMBOK EARTHQUAKE Classification Model Typical approach: Train and test on data from the same disasters
  23. 23. SVM (20 iterations 5- fold cross validation) Features P R F 0.81 0.81 0.81Statistical Features PRECISION RECALL F-MEASURE TRAIN & TEST ON SAME CRISES EVENTS What if we add some domain knowledge?
  24. 24. SEMANTIC INFORMATION <dbp:Barack_Obama> American dbprop:nationality <skos:Nobel_Peace_Price_laureates> dcterms:subject <dbo:PresidentOfUnitedStateofAmerica> rdf:type <dbp:Hosni_Mubarak> <skos:PresidentsOfEgypt> <dbp:CNN> <dbp:Egyptian_Arabic> <skos:Arab_republics> <dbp:Egypt> <skos:English-language_television_stations> dcterms:subject dcterms:subject dbprop:languages <dbp:Country> rdf:type rdf:type
  25. 25. Filtering out abstract concepts ADDING SEMANTICS TO CLASSIFICATION MODEL Semantic Annotation Semantic Expansion Semantic Filtering v
  26. 26. SVM (20 iterations 5- fold cross validation) Features P R F 0.81 0.81 0.81 - 0.82 0.82 0.82 1.39 0.81 0.81 0.81 0.33 0.82 0.82 0.82 0.6 Semantic Features Statistical Features PRECISION RECALL F-MEASURE ∆F /F (%) Semantic Features Semantic Features TRAIN & TEST ON SAME CRISES EVENTS
  27. 27. CLASSIFICATION MODELS HURRICANE HARVEY HURRICANE IRMA KERALA FLOODS LOMBOK EARTHQUAKE How can we train models to become less biased towards specific disaster events, or type of events? ? TYPHOON TRAIN CRASH BOMBING MASS SHOOTING TSUNAMI ? ? Classification Model
  28. 28. 41 8% 16% 32%8% 8% 4% 4% 4% 4% 8% 4% Wildfire/Bushfire E’quakes Flood/Typhoons Terror Shooting/Bombing Train Crash Meteor Haze Helicopter Crash DISTRIBUTION OF CRISES EVENT TYPES
  29. 29. CLASSIFYING FAMILIAR EVENTS Train model on all data, then test on a new crisis event of a type the was in the training set Eg., train model on data that include flood events, then test on a new flood crisis event Adding semantic features offer modest improvements over statistical features alone 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 TyphoonYolanda TyphoonPablo AlbertaFlood QueenslandFlood ColoradoFloods PhilippinesFlood SardiniaFlood GuatemalaEarthquake ItalyEarthquake BoholEarthquake CostaRicaEarthquake average F-Measure Statistical Features Semantic Features Flood/Typhoon Earthquake ∆ 1.7%
  30. 30. CLASSIFYING UNFAMILIAR EVENTS Train model on certain type of events, and test it on other types E.g., train model on data that include flood and earthquake events, then test on a train crash incident Adding semantic features offer a good improvement over statistical features alone 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 LAAirportShoot LacMeganticTrainCrash BostonBombing SpainTrainCrash TyphoonYolanda TyphoonPablo AlbertaFlood QueenslandFlood ColoradoFloods PhilippinesFlood SardiniaFlood GuatemalaEarthquake ItalyEarthquake BoholEarthquake CostaRicaEarthquake average F-Measure Statistical Features Semantic Features Terror/Bomb/Train Flood/Typhoon Earthquake Khare, P.; Burel, G. and Alani, H. Classifying Crises-Information Relevancy with Semantics. Extended Semantic Web Conference (ESWC), Heraklion, Crete, 2018. ∆ 7.2%
  31. 31. 0 10 20 30 40 50 60 70 80 90 100 Colorado W ildfire CostaRica Quake Guatem alaQuake ItalyQuake PhilippinesFlood Typhoon Pablo VenezuelaRefinery Alberta Flood Australia Bushfire BoholE’quake Boston Bom bing BrazilClub Fire Colorado Floods Glasgow Helicopter LA AirportShoot LacM eganticTrain M anilaFlood NYTrain Crash Queensland Flood RussiaM eteor Sardinia Flood SavarBuilding SingaporeHaze Spain Train Crash Typhoon Yolanda TexasExplosion L’AquilaQuake GenovaFlood Em iliaQuake Chile Quake ENGLISH ITALIEN SPANISH OTHER MULTILINGUALITY IN CRISES DATA
  32. 32. CLASSIFYING MULTILINGUAL CRISES DATA Monolingual Classification with Monolingual Models Cross-lingual Classification with Monolingual Models Train the model on one language and test it on data in the same language. For example, train and test on data written in English. This is the default approach, and can be used as a baseline. Run the classifiers on crisis data in languages that were not observed in the training data. For example, we test the classifier on Italian when the classifier was trained on English or Spanish. Cross-lingual Classification with Machine Translation Train the classification model on data in a certain language (e.g. Spanish), and use it to classify data that has been automatically translated from other languages (e.g., Italian and English) into the language of the training data.
  33. 33. Khare, P., Burel, G., Maynard, D., and Alani, H. Cross-Lingual Classification of Crisis Data. Int. Semantic Web Conference, Monterey, CA, USA, 2018 Around 9% improvement in detecting crisis-data relevancy when training on one language and applying it on another 0.429 0.688 0.521 0.64 0.578 0.489 0.5570.572 0.659 0.538 0.631 0.65 0.543 0.599 English [Italian] English [Spanish] Italian [English] Italian [Spanish] Spanish [English] Spanish [Italian] average Cross-lingual Classification with Monolingual Models Machine translation offers good classification improvements without any semantics 0.546 0.669 0.572 0.609 0.675 0.593 0.633 0.581 0.664 0.551 0.582 0.683 0.571 0.605 English [Italian- >English] English [Spanish- >English] Italiant [English- >Italian] Italiant [Spanish- >Italian] Spanish [English- >Spanish] Spanish [Italian- >Spanish] average Cross-lingual Classification with Machine Translation Semantics add little/no benefit when building, and applying, classification models on the same language 0.831 0.709 0.781 0.774 0.818 0.712 0.776 0.769 English [English] Italian [Italian] Spanish [Spanish] average Train language [Test language] Statistical Features Semantic Features Monolingual Classification with Monolingual Models
  34. 34. Task 1 Crisis vs. non-Crisis Related Messages Task 2 Type of Crisis Task 3 Type of Information Differentiate those posts that are related to a crisis situation vs. those posts that are not Identify the different types of crises the message is related to Differentiate those posts that are related to a crisis situation vs. those posts that are not Granularity CRISIS-DATA PROCESSING TASKS Shooting, Explosion, Building Collapse, Fires, Floods, Meteorite Fall, etc. Affected Individuals, Infrastructures and Utilities, Donations and Volunteer, Caution and Advice, etc. Olteanu, A., Vieweg, S., Castillo, C. What to Expect When the Unexpected Happens: Social Media Communications Across Crises. ACM Comp. Supported Cooperative Work and Social Computing (CSCW), 2015
  35. 35. CRISIS-DATA PROCESSING TASKS Incorporating semantics into Machine Learning classification methods: Approach 2: Deep LearningApproach 1: Traditional ML Classifiers
  37. 37.
  38. 38. DEEP LEARNING FOR CRISIS EVENT DETECTION A semantically-enriched deep learning model for event detection on Twitter Tweets Preprocessing Concept Extraction Word Vectors Initialisation Sem-CNN Training Pre-trained Embeddings Semantic Vectors Initialisation Bag of Words Bag of Concepts T = “Obama attends vigil for Boston Marathon bombing victims” W = [obama, attends, vigil, for, boston, marathon, bombing, victims] C = [obama, politician, none, none, none, boston, location, none, none, none] Term-Document Vector (Term Presence) Embeddings obama politician boston location ... ... ... ... none obama attends vigil for boston marathon bombing victims 1 1 1 1 0 0 0 0 1 Concepts Vector DEEP LEARNING MODEL Affected Individuals, Infrastructures and Utilities, Donations and Volunteering, Caution and Advice, Sympathy and Support, Other Useful Information (Olteanu et al 2015)
  39. 39. CLASSIFYING TWEETS WITH DEEP LEARNING SVM (TF-IDF): A linear kernel SVM classifier trained from the words’ TF- IDF vectors extracted from our dataset SVM (Word2Vec): A linear kernel SVM classifier trained from the Google pre- trained 300-dimensional word embeddings SEM-DL: Semantic Deep Learning approach Data is from CrisisLexT26: 26 crisis events, with 1,000 annotated tweets for a total of around 28,000 tweets. Data is too small for Deep Learning, hence only a proof of concept 0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64 Precision Recall F1 SEM-DL SVM (Word2Vec) SVM (TF-IDF) Burel, G.; Saif, H. and Alani, H. Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media. Int. Semantic Web Conf. (ISWC), Vienna, Austria, 2017.
  40. 40. CREES automatically processes short texts in a Google sheet, and identifies if a text is about a crisis, crisis-types and information-types Uses Deep Learning methods Google Sheet Add-on CRISIS EVENT EXTRACTION SERVICE Burel, G. & Alani, H. Crisis Event Extraction Service (CREES) - Automatic Detection and Classification of Crisis-related Content on Social Media. 15th Int. Conf. on Info. Sys. for Crisis Response and Management, Rochester, NY, USA, 2018
  41. 41. = CREES_RELATED(A1:A2) = CREES_TYPE(A1:A2) = CREES_INFO(A1:A2)
  42. 42. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Affected Individuals Caution & Advise Donation & Volunteering Infrastructure Sympathy Other useful information Hurricane Harvey Hurricane Irma Kerala Floods Hurricane Florence 36% 9% 15% 7%2% 11% 20% Relevant Caution & Advise Donation & Volunteering Affected Individuals Infrastructure Sympathy Other useful information DISTRIBUTION OF INFORMATION TYPES
  43. 43. RUMOURS v
  45. 45. DEEP LEARNING RUMOUR VERACITY CLASSIFIER Can work without waiting for responses (e.g., comments, retweets) Does not require the reactions (stances) given by the responses -- stance detection may introduce noise Makes use only of the source tweet
  46. 46. CHATBOTS FOR CRISES REPORTING Potential vs Reality On FB Messenger alone, there are currently over 300K active bots, exchanging over 8 billion messages between people and businesses each month.
  48. 48. What kind of issue would you like to report? Good afternoon first of all Oh my, I'm not programmed to understand what you're saying. Sorry! CHATBOTS – A LONG WAY TO GO Visits to the Facebook chatbot Visitors who clicked around in chatbot Users not following user flow Users tried to follow user flow Technical fault when submitting Reports successfully sent to Uchaguzi Total reports submitted through Twitter, SMS, onsite reporters Reports structured, geolocated, verified, and published 6875 687 3034 1501 1150 222 106 55 CHATBOT STATS PLATFORM STATS 65% 35% CHATBOT USER DEMOGRAPHICS
  49. 49. WHAT’S NEXT Inclusiveness of social media Biases: gender, technology, social media platform, language Usage of social media can differ across countries, cultures, genders, platforms, economies … How can we encourage, and direct, a better and more sustained crowdsourcing during disasters Many tools and services: when and how they need to be orchestrated and used Relevancy and value of social media crisis data is subjective and person/time dependent
  50. 50. Free, A.I. powered tools are now available, to: • Separate relevant from rubbish tweets, in ”multiple languages”, and for “any” type of crisis • Identify the category of crisis information they hold • Measure their veracity ”.. I would suggest, then, that the formula for the next 10,000 start-ups is very, very simple, which is to take x and add AI. That is the formula, that's what we're going to be doing. And that is the way in which we're going to make this second Industrial Revolution” Kevin Kelly, IBM
  51. 51. Gregoire Burel Lara Piccolo Prashant Khare Acknowledgements