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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
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
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
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
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
 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
 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)
 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)
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
 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
 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
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)
 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
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, 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)
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%), 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)
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,
Louisiana
 Active tweet account: FEMA, Red Cross,
RedCrossSELA (South East Louisiana)
ISCRAM 2013, May 12-15 19
4. Tweet Details (9/5-9/7)
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
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…
 ??
 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
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 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%
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

  • 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. 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. 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. 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. 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.  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.  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.  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. 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.  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.  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. ISCRAM 2013, May 12-15 12 3. Tweet Visualization WHAT WHEN WHERE WHO
  • 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. ISCRAM 2013, May 12-15 14 4. PhaseVis in Action (8/23-8/24)
  • 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. ISCRAM 2013, May 12-15 16 4. PhaseVis in Action (8/28-8/29) - Mainly in Louisiana, Mississippi, Georgia -
  • 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. ISCRAM 2013, May 12-15 18 4. PhaseVis in Action (9/5-9/7) - US continued -
  • 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. 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. 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.  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
  • 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. ISCRAM 2013, May 12-15 26 3. Visualization: Phase View
  • 27. ISCRAM 2013, May 12-15 27 Overview Detail 3. Visualization: Social Network View
  • 28. ISCRAM 2013, May 12-15 28 3. Visualization: Location View
  • 29. ISCRAM 2013, May 12-15 29 Is_R (Retweet check) Tweet Text Phases Date 3. Visualization: Tweet View
  • 30. Use Case & Demo http://spare05.dlib.vt.edu/~ctrvis/phasevis/ind ex_may.html ISCRAM 2013, May 12-15 30
  • 31. ISCRAM 2013, May 12-15 31
  • 32. ISCRAM 2013, May 12-15 32

Editor's Notes

  1. 8/28 morning – reached hurricane strength
  2. Goal: finding four phases in disaster tweets
  3. (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’