1. Disaster Data Informatics
for Situation Awareness
Ashutosh Jadhav
ashutosh@knoesis.org
Ohio Center of Excellence in Knowledge Enabled
Computing (Kno.e.sis)
Wright State University, Dayton, OH

2. Research Problem

3. Disaster Data Informatics for
Situation Awareness
Expedite decision making process in the disaster situation by
identifying useful/actionable information from social media
1. Informativeness Analysis
a. Identify information rich tweet messages (filtering noisy tweets)
based on variety of analysis
2. Classifying information rich messages
a. People at the disaster site, suffering people asking for help
b. Global response about the disaster (opinions, comments, news
etc.)
3. Expedite decision making process and situational awareness
a. Considering (2.a) understand needs at disaster site
b. Make connection resource-->needs

4. Informativeness Analysis

5. Motivation: Information
Overload
●
● 5,500 tweets per seconds during japanese earthquake and tsunami
***Within a minute of the quake, there were more than
40,000 earthquake-related Tweets. The micro-blogging site
said it hit about 5,500 Tweets per second on the quake......
-The New York Times
How to find useful and actionable
information quickly from such huge stream
of incoming event data?

6. Multidimensional
Event Analysis

7. Data generated at the Data generated
Dimensions disaster location around the world
MultidimensionalNGO
Affected people, data
Who generates the data?
(People) volunteers
People not directly
involved in the diaster
Reports about -Opinions, concerns,
- current situation, sympathy, desire for help
What data is - needs for resources,
generated? - medical & other -Sharing of related news,
(Content) emergencies blogs and other
- complains etc. multimedia
- Social media (Twitter, FB) Majorly through social
How the data is media (Twitter, Facebook,
- SMS and Web reports to
generated? blogs, etc)
involved NGOs and
(Network)
government organization
- Seeking for help Sharing personal view-
Why data is generated?
(Intention) - Inform current situation, needs points on the disaster
etc related incidents
When data is generated After the disaster, in recovery Mostly after the disaster
(Time) and rebuild phase

8. Research problem
How can we identify
useful/ informative (actionable) information
that can be used to
expedite decision making & situational awareness
in the disaster situation?

9. Approach

10. Informativeness Analysis
- Definition
● Useful/actionable information in the disaster situation
that can help for better and faster situation awareness

11. Examples messages
We need tent, cover, rice. Uneted Nation never Help us since the
earthquake, we live in Carre-four, Lapot street,
if women and children are victim of rape or other agressions in provisionnal
shelter, what number can we call to have fast assistance.
We are still under the sheets. We do not have: Tents, prelates, sanitary
articles and household etc. Bastien the city Alix fontamara 27
we don't have some water in the delmas camp 40b
We need tent indelmas 18 because we don't find nothing in the area.
How can we find help and food in fontamara 43 rue menos
A father, whose wife passed away, and has two children who need medical
attention. One child has a broken arm, and he is afraid of infection

12. Data generated at the Data generated
Dimensions disaster location around the world
Multidimensional data
Who generates the
data? (People)
Affected people, NGO volunteers People not directly involved in the
disaster
-Opinions, concerns, sympathy,
Reports about desire for help
What data is - current situation, -Sharing of related news, blogs
generated? - needs for resources, and other multimedia
- medical & other
(Content)
emergencies
- complains etc.
- Social media (Twitter, FB) Majorly through social media
How the data is (Twitter, Facebook, blogs, etc)
generated? - SMS and Web reports to involved NGOs
(Network) and government organization
Why data is - Seeking for help Sharing personal view-points on
- Inform current situation, needs etc the disaster related incidents
generated?
(Intention)
After the disaster, in recovery and rebuild Mostly after the disaster
When data is phase
generated
(Time)

13. Data set
● Social Networking Messages
○ Twitter, Facebook
● News articles
○ News websites, external links from tweets, FB status
● NGO messages
○ Ushahidi messages/reports
● Mobile messages
○ SMS

14. Informativeness Analysis
● Structure and syntactic analysis
● Linguistic analysis
Content Analysis ● Text analysis
● Metadata Analysis
● Author profile description
● Social connectivity
People Analysis ● Activity level
● Author credibility/influence
● Content analysis
● Social share analysis
News Analysis ● URL credibility
● Alexa analysis
● Content annotation using disaster domain model
considering:
Semantic Analysis entities mentioned, needs, resources, location,
organizations, people, disaster type etc.

15. Content Analysis
● Structure and syntactic analysis
○ Message length
○ Number of words, special characters, slags, dictionary words
● Linguistic analysis
○ Number of nouns, verbs, adverbs, adjective
○ POS patterns
● Text analysis
○ N-gram analysis
○ TF_IDF statistics
○ Entities (dbpedia/ontology)
● Metadata analysis
○ Publish time
○ Location (explicit and implicit)

16. People Analysis
● Author profile description
○ Profession
○ Demographic information (age, gender, location)
● Social connectivity
○ Number of follow-followers
● Activity level
○ Number of tweets
○ Number of tweets "on topic"
● Author credibility/influence
○ Klout
○ SocialMatica
○ Peer index

17. News Analysis
● News and other event related stories are generally linked in many
of the event related messages (tweets, etc.) primarily
○ Message size limitation (140 characters for Twitter)
○ Bringin external authoritative context
● Analyzing news and other event related stories plays a crucial role in
event analysis
Many news stories about the event
■ which news stories to focus on?
■ how to extract useful and actionable information
nuggets from these news stories ?

18. News Analysis
- Structure and syntactic analysis
Content Analysis - Linguistic analysis
- Text analysis
- Metadata Analysis
- Number tweets, retweets
- Facebook share, like, comments,
Social share analysis recommendations
- Google plus, LinkedIn shares
- Google page rank
URL credibility
- Local credibility (?)
Alexa analysis - Alexa global and country rank
(Alexa is a web information - Alexa url authority
company) - Alexa url & subdomain mozRank
- Alexa page & domain authority

19. Semantic Analysis
● Content annotation using disaster domain model
considering variety of entities mentioned (DBPedia)
○ needs, resources, location, organizations, people,
disaster type etc.

20. Semantic Disaster Model***
Reuse/ (formalise and build) disaster domain model considering:
Earthquake, floods, terror attack (disaster type will help us
Disaster type
for better understanding of needs)
Model of basic human needs needs in disasters like food,
Needs
water, medicines, shelter, etc
Model of resources which can satisfy some need like need:
Resources thirsty -> resource: water, fruit juice, need: hungry ->
resource: food etc.
Location Location of incidents, geo-location data
Organization Involved government and non-government organizations
Model of people base on gender, age group, role (mother,
People & father, son, etc.) (This can be help in
social role understanding/reasoning needs like if there is mention of
mother and baby then need may be milk)

21. QCRI Internship Proposal