This document summarizes a research paper on PhaseVis, a visualization tool for analyzing tweets during emergencies according to the four phases of emergency management: preparedness, response, recovery, and mitigation. It collected tweets about Hurricane Isaac in 2012, classified them into phases using machine learning techniques, and visualized the tweets on an interactive web interface. The tool aimed to help emergency managers understand public needs and situational awareness over time, but had limitations from only analyzing English tweets and a small dataset.

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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

2. Outline
1. Motivation
2. Hurricane Isaac
3. Approach (Selection, Classification, Visualization)
4. PhaseVis in Action
5. Limitations
6. Discussion
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3. 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

4. 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

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
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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)
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3. Building a Dataset (1/2)

8.  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

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
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3. Classification Algorithms

11.  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

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
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3. Visualization Implementation

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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
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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 -

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)…
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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 -

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)
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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…
 ??

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
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Acknowledgment

23. Thank you!
Questions?
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24. Supplementary
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25. 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

30. Use Case & Demo
http://spare05.dlib.vt.edu/~ctrvis/phasevis/ind
ex_may.html
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