Prepare, Manage, and Understand Crisis Situations using Social Media Analytics

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Presentation of Sven Schaust, Max Walther and Michael Kaisser on the topic "Prepare, Manage, and Understand Crisis Situations using Social Media Analytics" at ISCRAM2013

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Prepare, Manage, and Understand Crisis Situations using Social Media Analytics

  1. 1. Prepare, Manage, and Understand Crisis Situations using Social Media Analytics Sven Schaust, Max Walther and Michael Kaisser AGT International, Germany ISCRAM 2013 in Baden-Baden – May 12-15, 2013
  2. 2. 2 Outline 1. Introduction & Context • Social Media Analysis in a C2 Center 2. The “Avalanche” event detection approach • Identify posting “hot spots” • Evaluate post clusters with Machine Learning approach 3. Evaluation 4. Outlook
  3. 3. 3 Urban Management & Public Safety • Cites today are complex and need to be organized • Administration is responsible for keeping population safe • emergency services • health services • fire fighters • police Command & Control Center
  4. 4. 4 Urban Management & Public Safety Why is Social Media relevant in this context? ?
  5. 5. 5 Urban Management & Public Safety Why is Social Media relevant in this context? “There's a plane in the Hudson. I'm on the ferry going to pick up the people. Crazy”
  6. 6. 6 Urban Management & Public Safety Why is Social Media relevant in this context? “De tering, wat een hel!!! 1,4 miljoen mensen op dat terrein! #loveparade”
  7. 7. 7 Urban Management & Public Safety Why is Social Media relevant in this context? “#Hoboken is on fire. Building above Hoboken Farm Corporation at 300 Washington is all smoked out”  Social Media can help creating a situational awareness picture
  8. 8. 8 • detect, classify and display events to operator • accidents, fires, violence, demonstrations 1. Automatic detection of breaking events • improve USAP by focused Social Media Analytics • possibly contact owner of posts for more information 2. Monitoring of ongoing situations • automatic report generation • interactive investigation support 3. Post Incident reporting Context: Social Media in a C2 Center
  9. 9. 9 What do people tweet during disasters? Hurricane Sandy (NYC Region, October 2012) • Evaluated Tweets for period 10/25 – 10/31 • Total number of Tweets per day ~ 3 Mio. • Checked for Tweets about „sandy“, „hurricane“, „storm“, „evacuation“, „flood“, „building“ „collapsed“, „power“, „outage“, „fire“. Examples of Events (semi-automatic evaluation) • A crane collapsing on a construction site near 57th street • A part of an apartment house collapsing in Borough Park, Brooklyn • A fire in Breezy Point, Queens • Flooded tunnels, streets, apartments in various areas • Power outages in various areas
  10. 10. 10 Crane Event overall 950 tweets were found for Oct. 29th • 29.10.2012 18:41:56; Wow. Right down the street from me. #Sandy-damaged crane on new 57th St. hi-rise dangling in wind. • 29.10.2012 18:46:20; Be careful on West 57th St as there is a crane dangling from the rooftop! #HurricaneSandy #Sandy #NYC • 29.10.2012 18:50:31; From my window I can see the top of a crane hanging off, 60 stories up...not good news if that comes off #Sandy • 29.10.2012 18:57:17; Curious to see what happens with the dangling crane on 57th between 6th and 7th Staying clear of that area for a while #HurricaneSandy
  11. 11. 11 Breezy Point Fire overall 1406 tweets were found for Oct. 30th • 30 Oct 2012 01:51:11; A TV news crew covering the storm is trapped by rising water and nearby fire @ 147 Oceanside in Breezy Point - pls RT #sandy #fdny #nypd • 30 Oct 2012 03:19:35; There are several fires burning in Breezy Point and Broad Channel, but the FDNY cannot reach them because of the flooding. #sandy • 30 Oct 2012 06:00:58; Fire moving 130st street north and west toward Cronstant Ave in Rockaway. Fire at 209 street in Breezy. FDNY cannot get to Breezy. #sandy • 30 Oct 2012 22:16:16; Never seen anything like this in my life. #sandy @ Breezy Point, NY http://t.co/
  12. 12. 12 Avalanche: Event detection in a C2 Center
  13. 13. 13 Avalanche: Event detection in a C2 Center
  14. 14. 14 Avalanche: Event detection in a C2 Center
  15. 15. 15 Avalanche: Event detection in a C2 Center
  16. 16. 16 Avalanche: Event detection in a C2 Center
  17. 17. 17 Avalanche: Event detection in a C2 Center
  18. 18. 18 Two step approach: 1. Identify locations with high tweet activity • Collect geo-spatial tweet clusters 2. Evaluate clusters with a Machine Learning approach • Do these clusters constitute an real-world event that the tweeters are witnessing first-hand? Work in Progress: 3. Classify events according to type How is it done?
  19. 19. 19 Machine Learning – What is the task? = geo-located Social Media post (Tweet)
  20. 20. 20 Machine Learning – What is the task? • Suspicious package in #GrandCentral #NYC #bomb threat possibility not sure?? http://t.co/VwU7SP3X • Suspicious package found in Grand Central Station... the 456 train..the trains are closed !! [pic]: http://t.co/9YPki4k2 • Something happened in the #456 #trainstation in #GrandCentral #NYC http://t.co/GGKvQura • Accident on the #456train in #midtown #NYC http://t.co/fj2mJJmf vs. • RT @refinery29: This image of Madeleine Albright playing the drums will be the best thing you'll see today: http://t.co/rGwQ5RdG • «@_PrettyPoison Guess ill fill out more job apps today» make punna fill out some 2! • The Glamour & Glitz at the 2012 Emmy' s that we loved! http://t.co/CiTFszfL • @IszwanieSyahira: i'm happy and i hope u feel the same too. weeeee ~.~ • How to prepare yourself for Friday's apocalypse http://cnet.co/lPU We need to automatically determine which of the tweet clusters (tweets issued close to each other in a short time frame) represent real-world events and which are just random chatter.
  21. 21. 21 • We look for geo- spatial clusters of tweets (e.g. 3 or more tweets in a 200m radius, posted within 30 mins) • These become “event candidates” • Event candidates are evaluated with a Machine Learning scheme. • We currently use C4.5 decision trees. Architecture
  22. 22. 22 Machine Learning - Features Tweet cluster: • Suspicious package in #GrandCentral #NYC #bomb threat possibility not sure?? http://t.co/VwU7SP3X • Suspicious package found in Grand Central Station... the 456 train..the trains are closed !! [pic]: http://t.co/9YPki4k2 • Something happened in the #456 #trainstation in #GrandCentral #NYC http://t.co/GGKvQura • Accident on the #456train in #midtown #NYC http://t.co/fj2mJJmf
  23. 23. 23 Evaluation setup: • 1,000 hand-labeled tweet clusters. • 319 good, 681 bad. • 10-fold cross validation. Machine Learning - Evaluation
  24. 24. 24 Machine Learning - Evaluation Evaluation setup: • 1,000 hand-labeled tweet clusters. 319 good, 681 bad. • 10-fold cross validation. Unique Posters score CommonThemescore 110 Blue: event Red: no event
  25. 25. 25 If there are several tweets … • from roughly the same location • at roughly the same time • from different users • that nevertheless use the same words … chances are good that we have detected an event. (Somewhat simplyfied) Summary
  26. 26. 26 Outlook – what’s left to do? Derive more coordinates • from shared pictures • from toponyms in posts • use image sharing sites directly Make use of posts without coordinates • and add them to already existing clusters Explore real-time TF-IDF • to get rid of the Kardashians & Beliebers Evaluate system with real-world data • Because recall numbers are currently somewhat misleading
  27. 27. Thank you!

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