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PhaseVis: What, When, Where, and Who in Visualizing the Four Phases of Emergency Management Through the Lens of Social Media

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PhaseVis: What, When, Where, and Who in Visualizing the Four Phases of Emergency Management Through the Lens of Social Media

  1. 1. ISCRAM 2013, May 12-15 1 PhaseVis: Visualizing the Four Phases of Emergency Management Through the Lens of Social Media Seungwon Yang et al. Department of Computer Science, Virginia Tech 5/13/2013
  2. 2. Outline 1. Motivation 2. Hurricane Isaac 3. Approach (Selection, Classification, Visualization) 4. PhaseVis in Action 5. Limitations 6. Discussion ISCRAM 2013, May 12-15 2
  3. 3. 1. Motivation  Four Phases of Emergency Management Model  FEMA training material adds ‘Prevention/Protection’ http://training.fema.gov/EMIWeb/IS/IS230B/IS230bCourse.pdf ISCRAM 2013, May 12-15 3 Response Recovery Mitigation Preparedne ss
  4. 4. 2. Hurricane Isaac: Trajectory ISCRAM 2013, May 12-15 4 8/24 Cuba, Hispaniola: approx. 30 died 8/28-29 Mississippi River, Georgia, Port Fourchon, LA: 9 died After 8/30 N. Louisiana: degenerated to tropical depression *Image by Cyclonebiskit (Wikipedia) 8/21 Tropical storm Isaac 8/19-20 Extratropical cyclone
  5. 5. ISCRAM 2013, May 12-15 5 Disaster Tweets with emergency orgs, agency names Visualiza on & Interac on Manual Labeling Training Data Trained classifica on model Cleaned Tweets Original Tweets Original & Retweets Classified Tweets Select and Preprocess Tweets Classify into 4 phases Implement visualization & interaction 3. Overall Approach
  6. 6.  Tweet collection using ‘#isaac’ with yourTwapperKeeper  Situation report & Information sharing  Majority of tweets  Embedded URLs: news webpages, videos, photographs  Personal activity report  Very few ISCRAM 2013, May 12-15 6 3. Tweet Collection
  7. 7.  Approx. 56,000 English tweets collected with ‘#Isaac’  5,677 tweets (10%) with reference to Red Cross, FEMA, or Salvation Army  1,453 non-retweets  1,121 manually labeled with one of four phases (response, recovery, mitigation, preparedness) ISCRAM 2013, May 12-15 7 3. Building a Dataset (1/2)
  8. 8.  Tweet text + resource title ISCRAM 2013, May 12-15 8 Nice article abt our Dir. Of emerg srvcs @leopratte in #Louisiana organizing #redcross #Isaac relief http://t.co/D4RPr33n 3. Building a Dataset (2/2)
  9. 9. ISCRAM 2013, May 12-15 9 Response More than 4,700 people in as many as 80 shelters in 7 states overnight; more than 3,000 #RedCross workers (37 from KC region) at #Isaac Recovery FEMA announces that federal aid has been made available for the state of Louisiana. #Isaac Mitigation FEMA mitigations advisers to offer rebuilding tips in St. Bernard and Ascension Parishes. http://t.co/ZziRGOGw #Isaac Preparednes s Very cool app! MT @redcross: Our hurricane app has info on #RedCross shelters, a toolkit w flashlight, alarm http://t.co/E7o1rtJK #Isaac 3. Examples of 4 Phases
  10. 10.  SVM multiclass with linear kernel  Large num. of features, small num. of training examples  Naïve Bayes multinomial  Bag-of-words model fits well for tweet data  Random forest  One of the robust algorithms for text classification ISCRAM 2013, May 12-15 10 3. Classification Algorithms
  11. 11.  TF, normalization, stemming applied  Tuned classifier, 10 fold cross-validation ISCRAM 2013, May 12-15 11 Precision Weighted F Measure Naïve Bayes multinomial 77.87% 0.782 Random forest 76.27% 0.754 SVM multiclass (linear kernel) 80.82% Reported slightly lower than Naïve Bayes multinomial 3. Classification Cross-Validation
  12. 12. ISCRAM 2013, May 12-15 12 3. Tweet Visualization WHAT WHEN WHERE WHO
  13. 13.  WHAT (Phases, List)  Phases: ThemeRiver, D3 visualization toolkit  Tweet List: JqGrid Library  WHEN (Timeline)  JavaScript  WHERE (user locations)  Google Maps API  WHO (user mention network)  Gephi graph format, Sigma.js ISCRAM 2013, May 12-15 13 3. Visualization Implementation
  14. 14. ISCRAM 2013, May 12-15 14 4. PhaseVis in Action (8/23-8/24)
  15. 15.  Majority of tweets in Preparedness phase (84%)  Content: fill up the gas tank, hurricane App, preparedness tips, replace food/water in emergency kit, etc…  Clustered around  Red Cross, FEMA, & CraigatFEMA  Study focus was rather on the US (English tweets)  Spanish tweets from Cuba, Hispaniola not considered  Unable to understand phases in such areas ISCRAM 2013, May 12-15 15 4. Summary (8/23-8/24)
  16. 16. ISCRAM 2013, May 12-15 16 4. PhaseVis in Action (8/28-8/29) - Mainly in Louisiana, Mississippi, Georgia -
  17. 17.  High increase in tweet volume  Isaac landed in the US in 8/28 with hurricane strength  Response (20%), Recovery (34%), Mitigation (0%), Preparedness (46%)  Content:  Recruiting volunteers (Response, Recovery)  Asking for donations/support (Recovery)  RT regarding ‘Mitt Romney’  Providing shelters (Response)… ISCRAM 2013, May 12-15 17 4. Tweet Details (8/28-8/29)
  18. 18. ISCRAM 2013, May 12-15 18 4. PhaseVis in Action (9/5-9/7) - US continued -
  19. 19.  Mostly Recovery phase (75%), followed by continued Response actions…  Lots of activities in New Orleans, Baton Rouge, Louisiana  Active tweet account: FEMA, Red Cross, RedCrossSELA (South East Louisiana) ISCRAM 2013, May 12-15 19 4. Tweet Details (9/5-9/7)
  20. 20. ISCRAM 2013, May 12-15 20 5. Limitations  Language  Only English tweets considered  Unable to analyze Spanish tweets when Isaac hit Cuba & Hispaniola  Small data set  Only tweets containing FEMA, Red Cross & Salvation Army  E.g., RedCrossSELA, SalvationArmy, craigatFEMA, …  Approx. 10% of tweets had those names
  21. 21. ISCRAM 2013, May 12-15 21 6. Discussion  What are other valuable information to uncover from disaster tweets and why are they important?  Sentiment, Reliability of tweets  Embedded URLs: news articles, images, videos…  ??  To what extent can tweet analysis actually help emergency managers in the field?  Identification of ‘actionable’ tweets from affected areas, victims, and witnesses…  ??
  22. 22.  NSF for funding: IIS-0916733 (CTRnet project)  Internet Archive for collaboration  Big thanks to co-authors who couldn’t come here  Haeyong Chung, Xiao Lin, Sunshin Lee, Liangzhe Chen, Andy Wood, and the CTRnet Team ISCRAM 2013, May 12-15 22 Acknowledgment
  23. 23. Thank you! Questions? ISCRAM 2013, May 12-15 23
  24. 24. Supplementary ISCRAM 2013, May 12-15 24
  25. 25. Evaluation  Preprocessing & Accuracy ISCRAM 2013, May 12-15 25 TF IDF Normali zation Naïve Bayes Multinomial SVM Multiclass 76% 80.1% X 77% 80.4% X 60% 78.8% X X 78.1% X 75% 80.4% X X 78% 80.8% X X 63% 78.9% X X X 79.0%
  26. 26. ISCRAM 2013, May 12-15 26 3. Visualization: Phase View
  27. 27. ISCRAM 2013, May 12-15 27 Overview Detail 3. Visualization: Social Network View
  28. 28. ISCRAM 2013, May 12-15 28 3. Visualization: Location View
  29. 29. ISCRAM 2013, May 12-15 29 Is_R (Retweet check) Tweet Text Phases Date 3. Visualization: Tweet View
  30. 30. Use Case & Demo http://spare05.dlib.vt.edu/~ctrvis/phasevis/ind ex_may.html ISCRAM 2013, May 12-15 30
  31. 31. ISCRAM 2013, May 12-15 31
  32. 32. ISCRAM 2013, May 12-15 32

Editor's Notes

  • 8/28 morning – reached hurricane strength
  • Goal: finding four phases in disaster tweets
  • (QUESTION for Audience)Often ‘NULL’ title if attempts to access URLs after a month.Sometimes, title is almost the same as tweet contentAlso note the informal word usage: ‘abt’, ‘emerg’, ‘srvcs’
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