1.
Introduction to Machine Learning:
An Application to Disaster Response
Muhammad Imran & Shafiq Joty
Qatar Computing Research Institute
Hamad Bin Khalifa University
Doha, Qatar

2.
DISASTERS - SOCIAL MEDIA – RESPONSE EFFORTS
Humans suffering from the impacts of disasters, crises, and armed conflicts.
In the last two decades, 218 million people each year were affected by disasters;
At an annual cost to the global economy that exceeds $300 billion. (Source: UN)

3.
@NYGovCuomo orders closing of NYC bridges. Only Staten Island
bridges unaffected at this time. Bridges must close by 7pm. #Sandy
#NYC.
rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours
after they got separated from their mom when car submerged in si.
#sandy #911buff
freaking out. home alone. will just watch tv #Sandy #NYC.
400 Volunteers are needed for areas that #Sandy destroyed.
SANDY HURRICANE TWEETS

4.
@NYGovCuomo orders closing of NYC bridges. Only Staten Island
bridges unaffected at this time. Bridges must close by 7pm. #Sandy
#NYC.
rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours
after they got separated from their mom when car submerged in si.
#sandy #911buff
freaking out. home alone. will just watch tv #Sandy #NYC.
400 Volunteers are needed for areas that #Sandy destroyed.
Personal
Informative
SANDY HURRICANE TWEETS

5.
@NYGovCuomo orders closing of NYC bridges. Only Staten Island
bridges unaffected at this time. Bridges must close by 7pm. #Sandy
#NYC.
rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours
after they got separated from their mom when car submerged in si.
#sandy #911buff
freaking out. home alone. will just watch tv #Sandy #NYC.
400 Volunteers are needed for areas that #Sandy destroyed.
Personal
Informative
Caution and Advice
Reports of missing people
Help/volunteers needed
SANDY HURRICANE TWEETS

6.
@NYGovCuomo orders closing of NYC bridges. Only Staten Island
bridges unaffected at this time. Bridges must close by 7pm. #Sandy
#NYC.
rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours
after they got separated from their mom when car submerged in si.
#sandy #911buff
freaking out. home alone. will just watch tv #Sandy #NYC.
400 Volunteers are needed for areas that #Sandy destroyed.
Personal
Informative
Caution and Advice
Reports of missing people
Help/volunteers needed
SANDY HURRICANE TWEETS

7.
Personal
Informative
(Direct & Indirect)
Other
Caution and advice
Casualties and damage
Donations
People missing, found, or seen
Information source
Siren heard, warning issued/lifted etc.
People dead, injured, damage etc.
Money, shelter, blood, goods, or services
Webpages, photos, videos information sources
…
FINDING TACTICAL AND ACTIONABLE INFORMATION

8.
USEFUL INFORMATION ON TWITTER
Caution
and advice
Information
source
Donations
Causalities
& damage
A siren heard
Tornado warning issued/lifted
Tornado sighting/touchdown
42%
50%
30%
12%
18%
Photos as info. source
Webpages info. source
Videos as info. source
44%
20%
16%
Other donations
Money
Equipment, shelter,
Volunteers, Blood
38%
8%
54%
People injured
People dead
Damage
44%
44%
2%
16%
10%
% of informative tweets
Ref: “Extracting Information Nuggets from Disaster-Related Messages in Social Media”. Imran et al. ISCRAM-2013, Baden-Baden, Germany.

9.
INFORMATION PROCESSING PIPELINE (SUPERVISED LEARNING):
OFFLINE APPROACH
Data collection
1 2
Human annotations
on sample data
Machine training
3
Classification
4
Disaster Timeline:
DATA COLLECTION

10.
IMPACT AND RESPONSE TIMELINE
Department of Community Safety, Queensland Govt. & UNOCHA, 2011
Disaster response (today) Disaster response (target)
Target disaster response requires real-time processing of data.

11.
TIME-CRITICAL ANLYSIS OF BIG CRISIS DATA
Apply machine learningApply crowdsourcing

12.
REQUIREMENS & CHALLENGES
• Real-time analysis of data is required
• For rapid crisis response
• To reduce community harm
• Combine human and machine intelligence
• Usable and useful for end-users (mostly non-technical)
• End-users (stakeholders)
• Crisis managers (policy makers)
• Crisis responders (field workers)

13.
REQUIREMENS & CHALLENGES
Other key challenges:
• Volume
Scale of data (20m tweets in 5 days)
• Velocity
Analysis of streaming data (16k/min)
• Variety
Different forms/types of data (information types)
• Veracity
Uncertainty of data

14.
STREAM PROCESSING USING SUPERVISED ML
Combining human and machine computation
Quality assurance loops: human processing elements
do the work, automatic processing elements check for
consistency
Process-verify: work is done automatically, humans
check low-confidence or borderline cases
Online supervised learning: humans train the machine
to do the work automatically

15.
Data collection
1 2
Human annotations Machine training
3
Classification
4
ONLINE APPROACH
DATA COLLECTION
H
A
Learning-1
CLASSIFICATION OF DATA & DECISION MAKING PROCESS
Learning-2 Learning-3 … Learning-n
Human
annotation - 1
Human
annotation - 2
Human
annotation - 3 …
Human
annotation - n
First few hours
INFORMATION PROCESSING PIPELINE: ONLINE APPROACH
(REAL-TIME)

16.
http://aidr.qcri.org/
AIDR —Artificial Intelligence for Disaster Response— is a free, open-source, and easy-to-use
platform to automatically filter and classify relevant tweets posted during humanitarian crises.
1 2 3
Collect Curate Classify

17.
AIDR: FROM END-USERS PERSPECTIVE
Collection Classifier(s)
• Keywords, hashtags
• Geographical bounding box
• Languages
• Follow specific set of users
A collection is a set of filters A classifier is a set of tags
• Donations requests & offers
• Damage & causalities
• Eyewitness accounts
• …
2 step approach
1 2
http://aidr.qcri.org/

18.
AIDR APPROACH
Collection Classifier(s)
Tag Tag
Tag Tag
Learner
Classifier-1
Tag
Tag Tag Tag
30k/min
Classifier-2
http://aidr.qcri.org/

19.
AIDR: HIGH-LEVEL ARCHTECTURE
http://aidr.qcri.org/
Items Collector Feature Extractor Classifier(s)
Learner
Crowdsourcing
Task GeneratorStream of incoming
items from data sources
Item &
featuresItem
An expert defines
classifiers by giving
a name and description
for each category
Expert
Items
Crowd workers/volunteers
Model
parameter
Classified
Item
A list of classified items by category
and classifier’s confidence
Labeling
tasks
Labeled
item
Data
source
Data
source

20.
QUALITY VS. COST
http://aidr.qcri.org/
• Gaining acceptable quality
• Quality (classification accuracy)
• Cost (human labels: monetary in case of paid-workers, time in
case of volunteers)
Quality vs. cost using passive learning Quality vs. cost using active learning

21.
PERFORMANCE
http://aidr.qcri.org/
• In terms of throughput and latency
Throughput of feature extractor, classifier, and the system
Latency of feature extractor, classifier, and the system

22.
CHALLENGES: DOMAIN ADAPTATION
http://aidr.qcri.org/
• Crisis-specific labels are necessary
• Contrasting vocabulary use
• Differences in public concerns, affected infrastructure
• New labels should be collected for each new crisis
[ Imran et al. 2013b ]
• Domain adaptation
• Train models using all past labeled data (all types of events)
• Train on labeled data from past similar events
• Train on data from neighboring countries on similar events

23.
AIDR – COLLECTION SETUP
Collection detail dashboard
http://aidr.qcri.org/
Geographical region filterLanguage filter
Collection definition

24.
http://aidr.qcri.org/
AIDR – CLASSIFIER SETUP

25.
AIDR – CLASSIFIER SETUP (cont.)
http://aidr.qcri.org/

26.
AIDR – CROWDSOURCING-1
Internal Tagging Interface
http://aidr.qcri.org/

27.
AIDR – CROWDSOURCING-2
MicroMapper Interface (browser clicker)
http://aidr.qcri.org/
Mobile clicker

28.
AIDR – OUTPUT
http://aidr.qcri.org/
Training examples Classified output (achieved accuracy ~ 75%)

29.
- Killed 27 people
- A million evacuated
- $114 million of damage
TYPHOON HAGUPIT (2014)

30.
DEMO
http://aidr.qcri.org/

31.
AIDR has been awarded the Grand Prize in the
Open Source Software World Challenge 2015

32.
http://aidr.qcri.org/
AIDR —Artificial Intelligence for Disaster Response— is a free, open-source, and easy-to-use
platform to automatically filter and classify relevant tweets posted during humanitarian crises.
Thank you!