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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
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1. Motivation
 Four Phases of Emergency Management Model
 FEMA training material adds ‘Prevention/Protection’
http://training.fema.gov/EMIWeb/IS/IS230B/IS230bCourse.pdf
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Response
Recovery
Mitigation
Preparedne
ss

2. Hurricane Isaac: Trajectory
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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

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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
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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)
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3. Building a Dataset (1/2)

 Tweet text + resource title
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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)

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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
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3. Classification Algorithms

 TF, normalization, stemming applied
 Tuned classifier, 10 fold cross-validation
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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

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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
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3. Visualization Implementation

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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
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4. Summary (8/23-8/24)

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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)…
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4. Tweet Details (8/28-8/29)

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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)
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4. Tweet Details (9/5-9/7)

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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

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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
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Acknowledgment

Thank you!
Questions?
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Supplementary
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Evaluation
 Preprocessing & Accuracy
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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%

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3. Visualization: Phase View

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Overview Detail
3. Visualization: Social Network View

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3. Visualization: Location View

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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
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