Extracting Important Information from Social Network Stream During Crisis
Download as PPTX, PDF1 like1,535 views
1) The document discusses a project that aims to extract important information from social media streams during crises to help with disaster management efforts. 2) The first 24 hours after a natural disaster are most crucial, but getting accurate information is difficult, making it hard to respond effectively. The project seeks to address this by filtering social media data to find actionable information that can help reduce harm and save lives. 3) The methodology involves analyzing tweets from 2011 Queensland floods to identify important tweets based on elements like named locations, images, and keywords. Different combinations of these elements are weighted to prioritize potentially useful information for emergency responders.
More Related Content
Similar to Extracting Important Information from Social Network Stream During Crisis (12)
Recently uploaded (20)
Extracting Important Information from Social Network Stream During Crisis
- 1. Extracting Important Information from Social Network Stream During Crisis Avijit Paul ARC Centre of Excellence for Creative Industries and Innovation Queensland University of Technology a1.paul @ qut.edu.au @cdtavijit http://mappingonlinepublics.net/
- 2. Brief overview (2 minutes) Concept of the project (3 minutes) Experiment and result (10 minutes)
- 3. First 24 hours is the most crucial after a natural disaster IMPACT AND RESPONSE TIMELINE During first 24 hours getting the information is the hardest. Thus makes it difficult to act TARGET DISASTER MANAGEMENT With increased and verified information it is possible to reduce community harm and save lives Source: Department of Community Safety, Queensland Govt. 2011
- 5. Currently used tool Source: Yin, J., Lampert, A., Cameron, M., Robinson, B., & Power, R. (2012). Using social media to enhance emergency situation awareness.
- 6. WHAT AM I SLICING FOR? 1. Filter noise 2. Find actionable information 3. Priorities information Hendrickson, S. (2012a). Gnip The Social Cocktail, Part 2 Expected vs. Unexpected Events. Retrieved from http://blog.gnip.com/expected-足足vs足足unexpected-足足events-足足in-足足social-足足 media/
- 7. SEPERATING TWEETS No Category Reference Weight * 1 RT Retweet of a previously stored tweet -10 2 @reply Communication received from a high profile user 5 4 Named entity Name of a place not identified before 10 5 URL Uses a link to a news site 2 6 Instragram URL Is that a cat photo? +15 9 appeal word Included word help 10 *Weigh is not final
- 8. WHICH COMBINATION? @REPLY + HASHTAG= 78 RT + LOCATION = 75 NAMED ENTITY + TEMPORAL INFORMATION + KEYWORD = 180
- 9. METHOD & DATA Dataset: QLDflood 2011 (Queensland Flood) Method: Step 1: Read 4000+ tweets and identify tweets which I thought was important or not important for Emergency services. I found around 1000 tweets that had certain importance. 0 Our hearts go out to everyone affect by the #qldfloods 1 The Myer Centre entrance now sandbagged in Brisbane CBD. #QLDfloods http://twitpic.com/3p91v7
- 10. METHOD & DATA (cont) Step 2: Those 1000 tweets, I read to understand why I found them important. I found having Name of places (Named Entity), Image and Keyword (unigram) was more important to me Amazing photo: South Bank car basement full to the brim http://twitpic.com/3p8hg3 #QLDfloods #fb 3000 homes now underwater in Ipswich. Evacuations now include Bundamba Goodna Redbank Bellbird Park. #qldfloods Another pontoon floating down the Brisbane River #qldfloods http://twitpic.com/3p8g9t
- 11. METHOD & DATA (cont) Step 3: Do Bag of words feature extraction in Excel to identify if the tweets have any of the above mentioned words. Step 4: Multiple the existence with a score
- 12. Formula 1 Formula 3 Formula 2 Formula 4
- 13. Formula 1 and 2 (Image * 5 + Named Entity * 10 + keyword * 2 AND Image * 10 + Named Entity * 5 + keyword * 2) South Bank Ferris Wheel #bnefloods #qldfloods #qldfloodsmap http://twitpic.com/3p8olx Whoa @bazmeister: Eagle Street Pier Brisbane.. #qldfloods http://twitpic.com/3p79rx #BrisVenice
- 14. Formula 3 (Image * 2 + Named Entity * 5 + keyword * 10) Does anyone else find the name of the cafe floating down Brisbane river is "Drift"? http://goo.gl/N8PLT #qldfloods Concerns that a power generator in QUT Gardens Point has NOT been turned off - fears of electrocution in water. STAY AWAY #qldfloods Nine News: The iconic Drift Restaurant located on the Brisbane River has broken off and has now sunk so sad. #qldfloods Formula 4 (Image * 10 + Named Entity * 2 + keyword * 5) i'm going to twitpic some photos of the devastion the floods are having on people and families in queensland #prayforaustralia #qldfloods HOLY WOW Myer Centre flood preo. This is 20m from my work! #qldfloods #bnefloods http://twitpic.com/3p8wjt Riverside pathway at the cnr of the CBD botanic gardens #qldfloods http://twitpic.com/3p904f
- 15. FILTERING & PRIORITIZING 1. Combining other elements (name of places, images) with keyword (unigram) is better than identifying based on keyword alone 2. Emphasizing named entity (places) > images > keywords (unigram)
- 16. CHALLENGES 1. Finding named places is hard & well known problem 2. Images have challenges too
- 17. #QLDFLOOD TOP CATEGORIES URLS 4000 3500 3000 2500 2000 1500 1000 500 0 1400 1200 1000 800 600 400 200 0 2011 2013 SOURCE: Social Media in Crisis Communication (Burgess, Bruns & Paul) AOIR 2013
- 19. 8 pm uploaded 10 pm uploaded 9 PM uploaded 10 pm uploaded
- 20. Avijit Paul ARC Centre of Excellence for Creative Industries and Innovation Queensland University of Technology a1.paul @ qut.edu.au @cdtavijit http://mappingonlinepublics.net/