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Extracting Important Information from Social Network Stream During Crisis Avijit Paul ARC Centre of Excellence for Creative Industries and Innovation Queensland University of Technology a1.paul @ qut.edu.au @cdtavijit http://mappingonlinepublics.net/

Extracting Important Information from Social Network Stream During Crisis

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Paper Presented in IR14 (Internet research conference).

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Page 1: Extracting Important Information from Social Network Stream During Crisis

Extracting Important Information from Social Network Stream During CrisisAvijit Paul

ARC Centre of Excellence for Creative Industries and Innovation

Queensland University of Technology

a1.paul @ qut.edu.au

@cdtavijit

http://mappingonlinepublics.net/

Page 2: Extracting Important Information from Social Network Stream During Crisis

• Brief overview (2 minutes)

• Concept of the project (3 minutes)

• Experiment and result (10 minutes)

Page 3: Extracting Important Information from Social Network Stream During Crisis

During first 24 hours getting the information is the hardest. Thus makes it difficult to act

IMPACT AND RESPONSE TIMELINE

With increased and verified information it is possible to reduce community harm and save lives

TARGET DISASTER MANAGEMENT

First 24 hours is the most crucial after a natural disaster

Source: Department of Community Safety, Queensland Govt. 2011

Page 4: Extracting Important Information from Social Network Stream During Crisis

TARGET AUDIENCE

Page 5: Extracting Important Information from Social Network Stream During Crisis

Currently used tool

Source: Yin, J., Lampert, A., Cameron, M., Robinson, B., & Power, R. (2012). Using social media to enhance emergency situation awareness.

Page 6: Extracting Important Information from Social Network Stream During Crisis

WHAT AM I SLICING FOR?

Hendrickson, S. (2012a). Gnip The Social Cocktail, Part 2 Expected vs. Unexpected Events. Retrieved from http://blog.gnip.com/expected- vs- ‐ ‐unexpected- events- in- social- media/ ‐ ‐ ‐ ‐

1. Filter noise2. Find actionable

information3. Priorities

information

Page 7: Extracting Important Information from Social Network Stream During Crisis

SEPERATING TWEETS

No Category Reference Weight *

1 RT Retweet of a previously stored tweet -10

2 @reply Communication received from a high profile user 5

4 Named entity Name of a place not identified before 10

5 URL Uses a link to a news site 2

6 Instragram URL Is that a cat photo? +15

9 appeal word Included word “help” 10

*Weigh is not final

Page 8: Extracting Important Information from Social Network Stream During Crisis

WHICH COMBINATION?

NAMED ENTITY + TEMPORAL INFORMATION + KEYWORD =

180

RT + LOCATION

= 75

@REPLY + HASHTAG=

78

Page 9: Extracting Important Information from Social Network Stream During Crisis

METHOD & DATA

Dataset: QLDflood 2011 (Queensland Flood)

Method: Step 1: Read 4000+ tweets and identify tweets which I thought was important or not important for Emergency services. I found around 1000 tweets that had certain importance.

0 Our hearts go out to everyone affect by the #qldfloods

1 The Myer Centre entrance now sandbagged in Brisbane CBD. #QLDfloods http://twitpic.com/3p91v7

Page 10: Extracting Important Information from Social Network Stream During Crisis

METHOD & DATA (cont)

Step 2: Those 1000 tweets, I read to understand why I found them important.

I found having Name of places (Named Entity), Image and Keyword (unigram) was more important to me

Amazing photo: South Bank car basement full to the brim http://twitpic.com/3p8hg3 #QLDfloods #fb

3000 homes now underwater in Ipswich. Evacuations now include Bundamba Goodna Redbank Bellbird Park. #qldfloods

Another pontoon floating down the Brisbane River #qldfloods http://twitpic.com/3p8g9t

Page 11: Extracting Important Information from Social Network Stream During Crisis

METHOD & DATA (cont)

Step 3: Do Bag of words feature extraction in Excel to identify if the tweets have any of the above mentioned words. Step 4: Multiple the existence with a score

Page 12: Extracting Important Information from Social Network Stream During Crisis

Formula 1 Formula 2

Formula 3 Formula 4

Page 13: Extracting Important Information from Social Network Stream During Crisis

Formula 1 and 2 (Image * 5 + Named Entity * 10 + keyword * 2 AND Image * 10 + Named Entity * 5 + keyword * 2)South Bank Ferris Wheel #bnefloods #qldfloods #qldfloodsmap http://twitpic.com/3p8olx

Whoa @bazmeister: Eagle Street Pier Brisbane.. #qldfloods http://twitpic.com/3p79rx #BrisVenice

Page 14: Extracting Important Information from Social Network Stream During Crisis

Formula 3 (Image * 2 + Named Entity * 5 + keyword * 10)

Does anyone else find the name of the cafe floating down Brisbane river is "Drift"? http://goo.gl/N8PLT #qldfloodsConcerns that a power generator in QUT Gardens Point has NOT been turned off - fears of electrocution in water. STAY AWAY #qldfloodsNine News: The iconic Drift Restaurant located on the Brisbane River has broken off and has now sunk so sad. #qldfloods

Formula 4 (Image * 10 + Named Entity * 2 + keyword * 5)

i'm going to twitpic some photos of the devastion the floods are having on people and families in queensland #prayforaustralia #qldfloodsHOLY WOW Myer Centre flood preo. This is 20m from my work! #qldfloods #bnefloods http://twitpic.com/3p8wjtRiverside pathway at the cnr of the CBD botanic gardens #qldfloods http://twitpic.com/3p904f

Page 15: Extracting Important Information from Social Network Stream During Crisis

FILTERING & PRIORITIZING

1. Combining other elements (name of places, images)

with keyword (unigram) is better than identifying

based on keyword alone

2. Emphasizing named entity (places) > images >

keywords (unigram)

Page 16: Extracting Important Information from Social Network Stream During Crisis

CHALLENGES

1. Finding named places is hard & well known problem

2. Images have challenges too

Page 17: Extracting Important Information from Social Network Stream During Crisis

#QLDFLOOD TOP CATEGORIES URLS

Image/vi

deo sharin

gMedia

Facebook

Government

Unofficial in

fo reso

urce

Unofficial fu

ndraising

0500

1000150020002500300035004000

2011 2013Im

age/v

ideo sh

aring

Govern

ment

Media

Broke

nNGO

Faceb

ook

Utilities

Unofficial in

fo reso

urce0

200

400

600

800

1000

1200

1400

SOURCE: Social Media in Crisis Communication (Burgess, Bruns & Paul) AOIR 2013

Page 18: Extracting Important Information from Social Network Stream During Crisis
Page 19: Extracting Important Information from Social Network Stream During Crisis

8 pm uploaded9 PM uploaded

10 pm uploaded 10 pm uploaded

Page 20: Extracting Important Information from Social Network Stream During Crisis

Avijit PaulARC Centre of Excellence for Creative Industries and InnovationQueensland University of Technologya1.paul @ qut.edu.au@cdtavijithttp://mappingonlinepublics.net/