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UCAmI 2012 - Detection and Extracting of Emergency Knowledge from Twitter Streams

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UCAmI 2012 - Detection and Extracting of Emergency Knowledge from Twitter Streams

  1. 1. Detection and Extracting of Emergency Knowledge from Twitter Streams Bernhard Klein, Xabier Laiseca, Diego Casado- Mansilla, Diego Lopez-de-Ipiña and Alejandro Prada Nespral6th International Conference on Ubiquitous Computing and Ambient Intelligence Session 10: Key application domains: eEmergency, eLearning, eTraining 5. December, 2012 Social Awareness Based Emergency Situation Solver UCAmI 2012 B. Klein 1/17
  2. 2. Outline1. Problem Description2. Research Field3. Architecture of Analysis Tool4. Semantic Social Network Analysis5. Recent Advances6. Conclusions UCAmI 2012 B. Klein 2/17
  3. 3. Objective Trends Detection  Event Knowledge Extraction ≠ Counting of Keywords  Aggregation + Interpretation of post content! Problems:  Big data  Noisy + short posts  Real-time support UCAmI 2012 B. Klein 3/17
  4. 4. Twitter Examples► Good examples:► Bad examples:► Crawling reality: UCAmI 2012 B. Klein 4/17
  5. 5. Research Field • SensePlace2 • Hacer and Muraki, 2011 • TweetTracker • Sudha et al., 2011 • Twitcident Emergency Corpus Analysis Support Tools Microblogging SNA-Techniques Clustering-Techniques• Mendozza et al., 2010 • Becker et al, 2011 • Marcus et al, 2011 NLP-Techniques • Pohl et al, 2012 • Sudha et al, 2011 • Abel et al, 2011 UCAmI 2012 B. Klein 5/17
  6. 6. SABESS Web system UCAmI 2012 B. Klein 6/17
  7. 7. Opensource Implementation • Emergency message filter based on emergency taxonomy • Language filter e.g. english or spanish • Slang reduction (punctation + letter repititions) UCAmI 2012 B. Klein 7/17
  8. 8. Social Network Analysis► Objective: Filtering after tweet credibility UCAmI 2012 B. Klein 8/17
  9. 9. Observed Problems “Slow” Graph Calculations  Replace betweeness centrality with user data a) followers count ~ influence b) friend count ~ knowledge access c) number of posts ~ experience “Sparse” Social Network  Replace SNA with Sentiment Analysis: Punctation-, letter- and word repititions Tweet credibility < Informative tweet! (see also Sudha et al., 2011) UCAmI 2012 B. Klein 9/17
  10. 10. Natural language procesing► Objective: Content enrichment • Big Improvement with “slang reduction” !! UCAmI 2012 B. Klein 10/17
  11. 11. Other Knowledge Sources Hierarchical Knowledge Structure 1. Textual location a) Named Entity Location b) Regular Expression e.g. address (Requires reverse coding!) 2. Tweet metadata a) GPS tagged tweets b) Place tagged tweets (Author location can be different!) 3. User profile data a) Home location Increasing reliability! UCAmI 2012 B. Klein 11/17
  12. 12. Recent Advances: Event Detection► Objective: Group tweets into emergency events How to describe an emergency event?  Emergency type, location (range), time (progress), person/organization data, text descriptions, number of tweets  Global reporting standard “Common Alert Protocol”. Example: UCAmI 2012 B. Klein 12/17
  13. 13. Recent Advances: Clustering► Incremental DBSCAN SANDY HURRICANE RELIEF VOLUNTEER EFFORTS SANDY HURRICANE VICTIMS VOLUNTEER SANDY HURRICANE VICTIMS VOLUNTEER EFFORT GRASSROOTS SANDY HURRICANE VICTIMS VOLUNTEER NEWS EFFORT GRASSROOTSSandy, 180 SANDY HURRICANE VICTIMS POLICEFukuschima, 170 SANDY HURRICANE VICTIMS RELIEF…. SANDY HURRICANE VICTIMS Locations:Ambulance, 80 SANDY HURRICANE VICTIMS RELIEF DISASTER PrincetonHall e.g.….. SANDY HURRICANE RELIEF SANDY HURRICANE RELIEF SANDY HURRICANE RELIEF DISASTER SANDY VOLUNTEEROnline Conversations:Dictionary ConversationID=83 Hashtags: #TylerPerryFire Attachments: http://t.co/kqF7Xy8t UCAmI 2012 B. Klein 13/17
  14. 14. Common Alert Protocol Whenever clusters become modified, generate new alert message?? Alert CAP Info Place Urls, Figs Cluster of tweets UCAmI 2012 B. Klein 14/17
  15. 15. Conclusions Real-time analysis of noisy tweets ► Big data problem, 2 phase analysis  Emergency message filtering  Slang and language filtering ► Semantic Social Network Analysis  POS/Noun tags, NER/Location tags  Community centrality/follower count tags ► Tweet clustering  Group tweets after hashtags, attachments and conversations  Group tweets after emergency specific keywords ► Common Alert Protocol UCAmI 2012 B. Klein 15/17
  16. 16. Contact: Bernhard Klein, Email: bernhard.klein@deusto.es Deusto Intitute of Technology, University of Deusto, th International Conference on Ubiquitous Computing and Ambient Intelligence6 Avda. Universidades, 24 | 48007 Bilbao | Session 10: Key application domains: eEmergency, eLearning, eTraining Spain 5. December, 2012 UCAmI 2012 B. Klein 16/17

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