Keynote slides for ISCRAM 2016.
"Social Media platforms such as Twitter are invaluable sources of time-critical information. Information on social media communicated during emergencies convey timely and actionable information. For rapid crisis response, real-time insights are important for emergency responders. Although, many humanitarian organizations would like to use this information, however they struggle due a number of issues such as information overload, information vagueness, less credible and misinformation. In this talk, I will describe the role of social media and potential artificial intelligence computational techniques useful for humanitarian organizations and decision makers to make sense of social media data for rapid crisis response."
2. This Talk is About…
• The Role of Informa2on in Time-cri2cal Situa2ons
– Natural disasters and their destruc+ons
– Man-made disasters and mass convergence events
• The Role of Social Media for Disaster Response
– Par+cular focus on micro-blogging plaJorms
– Availability of various types of informa+on and opportuni+es
• The Role of Ar2ficial Intelligence for Disaster Response
– How AI is useful in disaster response
– Various AI techniques, approaches, and tools
– Work of crisis compu+ng group at QCRI
– Ongoing research
– Future direc+ons
47. Crowdsourced Stream Processing
Combining human and machine computa2on
Difficult, ambiguous
items to be labeled
by crowd
Automatic
processing
Automatic
processing
output output
Performing verification
Providing training data
a: Split automatic/manual processing b: Detect-verify paradigm
Automatic
processing
Automatic
processing
output
c: Improving quality through active learning
input input
input
Difficult, ambiguous
items to be labeled
by crowd
Automatic
processing
Automatic
processing
output
Performing verification
Providing training data
a: Split automatic/manual processing b: Detect-verify paradigm
Automatic
processing
Automatic
processing
output
c: Improving quality through active learning
input input
input
Difficult, ambiguous
items to be labeled
by crowd
Automatic
processing
Automatic
processing
output output
Performing verification
Providing training data
a: Split automatic/manual processing b: Detect-verify paradigm
Automatic
processing
Automatic
processing
output
c: Improving quality through active learning
input input
input
Quality assurance loops: human processing elements
do the work, automa+c processing elements check for
consistency
Process-verify: work is done automa+cally, humans
check low-confidence or borderline cases
Online supervised learning: humans train machines
to perform work automa+cally
49. AIDR: From End-users Perspec2ve
Collec2on Classifier(s)
• Keywords, hashtags
• Geographical bounding box
• Languages
• Follow specific set of users
A collec2on is a set of filters A classifier is a set of tags
• Dona2ons requests & offers
• Damage & causali2es
• Eyewitness accounts
• …
2 steps approach
1 2
hop://aidr.qcri.org/
57. High-level Architecture
hop://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
94. Conclusions
• Informa2on bestows power for disaster response
– People need informa+on as much as water, shelter, and food
– Disasters are unavoidable, but planning can lessen their effects
• Social media as 2me-cri2cal informa2on source
– Early warnings, event detec+on, event monitoring
– Availability of informa+on opens new opportuni+es
• Ar2ficial Intelligence for Disaster Response
– Applied research at its best
– AI + humans-in-the-loop can enable rapid crisis response
– AI techniques useful for:
• Situa+onal awareness
• Ac+onable informa+on extrac+on
• Summariza+on