ISCRAM 2013, May 12-15 1
PhaseVis:
Visualizing the Four Phases of
Emergency Management
Through the Lens of Social
Media
Se...
Outline
1. Motivation
2. Hurricane Isaac
3. Approach (Selection, Classification, Visualization)
4. PhaseVis in Action
5. L...
1. Motivation
 Four Phases of Emergency Management Model
 FEMA training material adds ‘Prevention/Protection’
http://tra...
2. Hurricane Isaac: Trajectory
ISCRAM 2013, May 12-15 4
8/24
Cuba, Hispaniola:
approx. 30 died
8/28-29
Mississippi River, ...
ISCRAM 2013, May 12-15 5
Disaster Tweets
with emergency
orgs, agency names
Visualiza on
&
Interac on
Manual
Labeling
Train...
 Tweet collection using ‘#isaac’ with
yourTwapperKeeper
 Situation report & Information sharing
 Majority of tweets
 E...
 Approx. 56,000 English tweets collected with
‘#Isaac’
 5,677 tweets (10%) with reference to Red Cross,
FEMA, or Salvati...
 Tweet text + resource title
ISCRAM 2013, May 12-15 8
Nice article abt our Dir. Of emerg srvcs @leopratte
in #Louisiana o...
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 ...
 SVM multiclass with linear kernel
 Large num. of features, small num. of training
examples
 Naïve Bayes multinomial
 ...
 TF, normalization, stemming applied
 Tuned classifier, 10 fold cross-validation
ISCRAM 2013, May 12-15 11
Precision Wei...
ISCRAM 2013, May 12-15 12
3. Tweet Visualization
WHAT
WHEN
WHERE
WHO
 WHAT (Phases, List)
 Phases: ThemeRiver, D3 visualization toolkit
 Tweet List: JqGrid Library
 WHEN (Timeline)
 Java...
ISCRAM 2013, May 12-15 14
4. PhaseVis in Action (8/23-8/24)
 Majority of tweets in Preparedness phase (84%)
 Content: fill up the gas tank, hurricane App,
preparedness tips, replac...
ISCRAM 2013, May 12-15 16
4. PhaseVis in Action (8/28-8/29)
- Mainly in Louisiana, Mississippi, Georgia -
 High increase in tweet volume
 Isaac landed in the US in 8/28 with hurricane
strength
 Response (20%), Recovery (34%),...
ISCRAM 2013, May 12-15 18
4. PhaseVis in Action (9/5-9/7)
- US continued -
 Mostly Recovery phase (75%), followed by
continued Response actions…
 Lots of activities in New Orleans, Baton Rouge,
L...
ISCRAM 2013, May 12-15 20
5. Limitations
 Language
 Only English tweets considered
 Unable to analyze Spanish tweets wh...
ISCRAM 2013, May 12-15 21
6. Discussion
 What are other valuable information to uncover from
disaster tweets and why are ...
 NSF for funding: IIS-0916733 (CTRnet project)
 Internet Archive for collaboration
 Big thanks to co-authors who couldn...
Thank you!
Questions?
ISCRAM 2013, May 12-15 23
Supplementary
ISCRAM 2013, May 12-15 24
Evaluation
 Preprocessing & Accuracy
ISCRAM 2013, May 12-15 25
TF IDF Normali
zation
Naïve Bayes
Multinomial
SVM Multicla...
ISCRAM 2013, May 12-15 26
3. Visualization: Phase View
ISCRAM 2013, May 12-15 27
Overview Detail
3. Visualization: Social Network View
ISCRAM 2013, May 12-15 28
3. Visualization: Location View
ISCRAM 2013, May 12-15 29
Is_R
(Retweet
check)
Tweet
Text
Phases Date
3. Visualization: Tweet View
Use Case & Demo
http://spare05.dlib.vt.edu/~ctrvis/phasevis/ind
ex_may.html
ISCRAM 2013, May 12-15 30
ISCRAM 2013, May 12-15 31
ISCRAM 2013, May 12-15 32
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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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Work-in-progress research presentation at ISCRAM'13, Baden-Baden, Germany. May12-15, 2013

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  • 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’
  • 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
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