Crisis Mapping, Citizen Sensing and Social Media Analytics: Leveraging Citizen Roles for Crisis Response Coordination

CrisisMapping,CitizenSensingand
SocialMedia Analytics
Hemant Purohit Amit Sheth Carlos Castillo Patrick Meier
The Ohio Center of Excellence in
Knowledge-enabled Computing (Kno.e.sis)
Wright State, USA
Qatar Computing
Research Institute (QCRI)
Doha, Qatar
Leveraging Citizen Roles for Crisis Response Coordination

Introduction: Kno.e.sis and QCRI
• At Kno.e.sis: NSF SoCS project on ‘Social Media Enhanced Organizational Sensemaking
during Emergency Response’
• At QCRI: ‘Artificial Intelligence for Disaster Response’ (AIDR) project for Social Innovation

Outline
• Introduction
• Gaps & Challenges
• Role of Computer Science
• Applied Crisis Computing
• Design Principles

EMERGENCY HACKATHONS AFTER HAITI DEVASTATION .. Thousands of miles away!

“YOUR SITE HELPED SAVE HUNDREDS OF LIVES”
- US MARINE CORPS

Digital Footprints of Twitterers..
.. Pulse of the planet

Why we care about Citizen Sensing?
- It forms Self Organizing Communities!

Crisis Response Coordination
UN Cluster
system

We all need to join
hands together for
effectively
improving
response
coordination!
- Humanitarians
- Computer and
Social Scientists

- Big Data in crisis situations
needs computing help!
- Humanitarians alone can’t handle it!

Outline
• Introduction
• Scale, velocity, redundancy, heterogeneity, bias, noise & verifiability

Puzzle of Crisis Informatics
• What emergency-responders want?
1. Any available prior knowledge about
the impact of similar past disasters in
the region?
2. Are existing response strategies
sufficient?
3. Which factors will worsen conditions?
4. How many fatalities? Extent of
damage?
What
emergency-
responders
want
What
computer
scientists can
provide
What is
supported by
current social
media data

• What computer scientists can provide?
• Algorithms to detect and predict abnormal
trends
• Semantic abstraction and summarization
of data
• Human+Machine readable knowledge
organization via ontologies
• Technology to map geo-located
information
• Visual data interface for quicker
comprehension
What
emergency-
responders
want
What
computer
scientists can
provide
What is
supported by
current social
media data

• What is supported by social media data?
• Real-time updates on the situation
• Textual summaries, images, videos
• Messages about needs and offers
• Geo-location metadata
What
emergency-
responders
want
What
computer
scientists can
provide
What is
supported by
current social
media data

Crisis Response Analytics
• Mainly three major methods of information extraction and
mapping:
• Manual feed (Processed info.) based
• e.g., Most of the formal and hybrid response organizations (Red Cross,
UNOCHA), Recovers.org, AIDMatrix, SparkRelief, etc.
• Crowdsourcing with limited automation
• e.g., Crowdmap/Ushahidi, etc.
• Automatized processing based
• e.g., Twitris, CrisisTracker, etc.
• Information management for resource coordination:
• e.g., Sahana

Illustrative Crisis Informatics
Projects
Project Host Team Focus
Sahana Univ. of Maryland Information Management
EPIC
(Tweak-the-Tweet)
Univ. of Colorado and UC
Irvine
Information extraction and
behavioral aspects in response
NSF SoCS Kno.e.sis, Wright State Univ.
and Ohio State Univ.
Organizational sensemaking and
Coordination
AIDR QCRI, Doha Targeted Information extraction
NSF GeoNets Univ. of Southern California Ad hoc Geospatial Data Sharing
Note that it is not an exhaustive list, see more resources here:
http://wiki.knoesis.org/index.php?title=Summary_about_Social_Media_Research_in_Disaster/Emergency_Response_Systems&oldid=5177

Illustrative Crisis Mapping and
Analytics tools
Tool
Visual
Geo
Mapping
Human
Inputs
Real-
time
Update
People to
engage
with
Topical
summary
Explore
data
Semantics
CrowdMap (Ushahidi) Y Y Y Y
Sahana Y Y Y Y Y
AIDMatrix Y Y
Recovers.org Y Y
SparkRelief Y Y Y
Twitris* Y Y Y Y Y
Crisis Tracker* Y Y Y
*Social Media driven Note that it is not an exhaustive list, see more resources here:
http://wiki.knoesis.org/index.php?title=Summary_about_Social_Media_Research_in_Disaster/Emergency_Response_Systems&oldid=5177

Tools: Sahana
• A free & open source portable web tool for Disaster Management
• Features:
• Organization Registry
• Maintains data (contact, services, etc.) of organizations and volunteers in
response
• Missing Persons / Disaster Victim Registry
• Helps track and find missing, deceased, injured and displaced people and families
• Request Management
• Tracks all requests and helps match pledges for support, aid and supplies to
fulfilment
• Shelter Registry
• Tracks data on all temporary shelters setup following the Disaster
More: http://www.slideshare.net/skbohra/sahana-disaster-management-system

Tools: Sahana (Organizationregistry)

Tools: CrowdMap
• The well-known Ushahidi’s version
• Geo-located reports
• Crowdsourced data pieces, turned into powerful information
nuggets as reports from regions
• Video:
• http://www.youtube.com/watch?v=GjPc39OXr6I

Tools: CrowdMap (Overview)
http://zombiejournalism.com/2010/09/how-to-build-manage-and-customize-a-crowdmap/

Tools Demo: Twitris
• Example of automatic processing compared to the previous
tool based on manual-feed processing for crisis computing
• A Semantic Social Web platform for comprehensive event
analysis
• Real-time monitoring and multi-faceted analysis of social signals:
• space, time, people, content, network, and additionally sentiment and
emotion
• Platform for on-going research for situational awareness and
coordination using social media and knowledge on the Web

Important tags to
summarize Big Data flow
Related to Oklahoma
tornado
Images and Videos Related
to Oklahoma tornado
Tools Demo: Twitris (Topicalnuggetsummary)

Incoming Tweets with need types
to give quick idea of what is
needed and where currently #OKC
Legends for
Different needs
#OKC
Tools Demo: Twitris (Real-timeinformationfor
needs)
Clicking on a tag brings contextual
information– relevant tweets,
news/blogs, and Wikipedia articles

Tools Demo: Twitris (Influencerstoengagewith,forspecific
needs)
Influential users are for
respective needs. Right side
shows their interaction
network on social media.
Engaging with influencers in the self organizing communities
can be very powerful for- a.) getting important information, b.)
Correcting rumors in the network, c.) Propagating important
information back into the citizen sensors community

Tools Demo: Twitris (InR&D:EngagementInterfacefor
responders)
What-Where-How-Who-Why
Coordination
Influential users to engage
with and resources for
seekers/supplies at a location,
at a timestamp
Contextual
Information for a
chosen topical
tags

Tools Demo: Twitris during Oklahoma-
Tornado disaster response
• Video of the on-going monitoring on the next morning of the
Oklahoma Tornado:
• http://twitris.knoesis.org/images/datasets-and-models/Twitris--for-
Oklahoma-disaster.mov
• Snapshots during the analysis:
• Images

Who are the people to
engage with in the evolving
ad-hoc social community?
Which needs are of
utmost importance?
Actionable information improves decision making process.
Who are the resource
seekers and suppliers?
Questions to social media tools
for Disaster Response Coordination
Where can I go for volunteering at
my location?
How and Where
can one donate?

Challenge: Heterogeneity
• Multiple channels
• Phone, fax, TV, radio, newspapers, internet, sensor
networks, etc.
• Coexistence of technologies, a constant
• Social media is heterogeneous
• Verified accounts
• Re-tweets from well-known sources
• Eyewitness reports
• Lots more!
• Different types (unstructured text, structured,
multimedia) may require different tools
http://blogs.lse.ac.uk

Challenge: Velocity
• Social media information is more valuable in the
first minutes and hours after a disaster
• Affected people are there before anybody else
• When emergency responders arrive, their priority
may not be to keep information flowing
• After hours/days social media is still valuable, but
there is much more information from other sources
• In the early hours of a disaster, television feels so
slow in comparison
• Often a few seconds of footage repeated over and
over and over
http://seventhinc.com/

Challenge: Scale
• In some countries a sizable fraction of the
population has Internet access
• Tweets are small and nimble but they point to
webpages, include images, videos, etc.
• You need to process a lot to obtain a little
• There are many tweets but
• Only some of them contain usable information
• Only a fraction of those can be handled by
automatic systems
Top-4 countries by
Twitter penetration
among Internet
users; by Comscore
via http://5mk.co/

Challenge: Redundancy
• Information from multiple information channels
may not be unique
• Near-duplicates frustrate users and waste their
time
• Definition of abstraction level (to merge items) is
always arbitrary, depends on the application
• Automatic systems tend to pick what is
redundant first
• Not necessarily a bad thing, e.g. phrases that are
often repeated, tweets that are often re-tweeted,
etc.
Millenial’s information
sources http://ypulse.com/

Challenge: Biases
• Social Media Bias:
• Youngers better user than elders
• Educated users more existent than uneducated
• Technology Privileged users more existent than unprivileged
• Study carefully, with the grains of salt!
• Smart sampling
• Smart data cleaning
• Smart algorithms

Challenge: Noise
• Everyone wants to be heard
• Independently of adding any value
• Emotional expressions and even jokes drive the data traffic
• Informal text and jargon hinders automatic text processing

Challenge: Verifiability
• Social media users are starting to develop
their own methods to validate information
• In crisis scenarios most rumors are spread
by well-intentioned people
• But there are also some pranksters
• We need a more fine-grained approach
than true/false (we have always needed it)
Edelman 2012
http://edelman.com/trust

Outline
• Introduction
• IR, DM, ML, NLP, SN, HCI

Information Retrieval (IR)
• The research field that created web search
• No problem working with subjective definitions
• Relevance has always been in the eye of the beholder
• Can help us by providing searching and ranking operations on
social media reports

IR Method: inverted indexes
• What does it do?
• Allows to locate documents
containing a term without
having to scan a whole
Collection
How does it work?
• An inverted index contains a list of terms, and a
list of documents containing each term
• How can it help us?
• Indexing a collection of reports can help us
locate specific ones very quickly
Encyclopedia of Language and Linguistics

IR/ML Method: learning-to-rank
paradigm
• Find relevant documents for a search
• How does it work?
• Modern methods use hundreds of
static (document-dependent) and
dynamic (query-document-dependent)
characteristics and a learning-to-rank
framework
• Modern IR is well beyond hard rules,
and beyond heuristic scoring functions;
no need to re-invent the wheel
http://people.dsv.su.se/~eriks/

IR Method: document clustering
• Group search results in order to better scan
them; can be done on a query-dependent or
query-independent way
• One way is to do a weighted dot product in
which weights are associated to how
informative (~rare) are words
• Makes it easier to deal with large, redundant
collections of text
http://carrot2.org/

Example of document clustering
Crisis Tracker

Data Mining (DM)
• The science of finding patterns in data
• Finding association rules, categories of elements, anomalies,
etc.
• Managing temporal data
• Can help us detect and track trends and topics
• Managing static data
• Can help us reduce the dimensionality of data

DM Method: burst detection
• Reliably identifies anomalies in a time series
(e.g. volume of tweets w/hashtag vs time)
• Look for increases above the norm; look for
change patterns that precede crisis
• In general it is hard over noisy signals
• Detection of sub-events in an ongoing crisis is
important to rapidly respond to them
Volume for query “boston” in
Google (trends.google.com).

DM Method: topic detection and
tracking
• Track the relative
popularity of different
topics over time
• Cluster documents per
time slice, merge across
times slices
• See emerging stories, track
new developments, sub-
stories, etc.
TextFlow

DM Method: dimensionality
reduction
• Represent complex data in simpler
terms
• Find independent pieces of
information, discard/merge
correlated ones
• We can focus on the big picture,
not just hash-tags and keywords,
but topics
4
dimensions
(x,y,z,color)
2
dimensions
(x’,y’)
X, Y axes are correlated => X’ axis
Z is independent => Y’
Color is equivalent to X’ => gone
X’
Y’
http://www.cs.otago.ac.nz/

IR/DM Method: reduce text
dimensionality
LDA. Illustration by Lisa M. Rhody
Input:
thousands of
dimensions
(one for
every word)
Output:
a handful of
dimensions (one
for every topic)

Statistical Machine Learning (ML)
• A branch of artificial intelligence
• While DM focuses on discovery, ML focuses on prediction
• ML aims at representing data and generalizing from it
• Supervised statistical machine learning is a well-established
framework to learn the relationship between inputs and
outputs
• Can help us learn from human labeling efforts to create
automatic labels for new data

ML method: supervised
classification
• Learn to separate different classes
of elements, given (relatively) few
examples
• Several methods to choose from,
popular ones are SVMs and
Decision Trees/Forests
• Automatic classification of reports
http://www.quora.com/

Example: automatic tweet
classification
Caution &
Advice
Information
Sources
Damage &
Casualties
Donations
Health
Shelter
Food
Water
Logistics
...
...

ML method: regression
• The same as supervised
classification but the target is
numerical, not categorical
• It learns the parameters of a
function that fits what is observed
• It can predict an outcome from
current data http://qcri.qa/

Natural Language Processing
(NLP)
• A research area that has fought
against several (possibly AI-complete)
problems
• Watson and other projects have
demonstrated visibly their success
• Can help us to classify and extract
information by doing automatically:
• Morphological analysis
• Dependency parsing
• Entity linking / Word sense
disambiguation
http://voices.washingtonpost.com/

NLP method: tagging
• Determines classes for tokens or segments
on a text: part-of-speech tags, named
entities
• Supervised learning with structured outputs
• A richer representation of tweets yields
better predictions
• Spotting named entities or key phrases can
help summarize tweets
I/preposition
can/modal
see/verb
the/determiner
flames/noun
from/preposition
here/adverb

NLP methods: dependency
parsing
• Identifies relationships between
different parts of a text
• Learned from labeled data using
structured output (output is a parse
tree)
• Identifying key elements on text can
help find cases where a named entity
is central on a report
“Bills on ports and immigration were
submitted by Senator Brownback,
Republican of Kansas”
http://nlp.stanford.edu/

NLP method:
disambiguation/linking
• Connect named entities to concepts, e.g. a
sense on a dictionary or a URL in Wikipedia
• Entities can have multiple senses; the
correct one is picked by using contextual
clues
• Once we have determined a concept we
can map it to broader classes
1 readiness to give attention
2 quality of causing attention to be given
3
activity, subject, etc., which one gives time
and attention to
4 advantage, advancement, or favour
5 a share (in a company, business, etc.)
6 money paid for the use of money
Meaning of “interest”
This may be of interest [2] to you
The money grows because of
compound interest [6]
http://www.ling.gu.se/~lager/

Graph Theory (GT) a.k.a.linkanalysis,networkanalysis
• Social graphs are important abstractions, they
represent social connections as a graph
• Lots of information can be derived from
properties of this graph
• Communities
• Central users
• Bridges
• Availability of large datasets from online social
networking sites has brought new life to this
field http://www.hackingalert.net/

GT method: graph clustering
• Find communities of densely
connected nodes
• There are many methods,
depending on the definition of
community
• We can identify groups of people
who are closely connected
http://griffsgraphs.com/

GT method: centrality metrics
• Identify which nodes in a graph are in
more shortest paths (centrality), or are
more likely to be at the end of a random
walk (PageRank)
• Pagerank is computed through iterative
calculations over the entire graph
• These are good proxies for importance
on a network
Wikipedia

Human-Computer Interaction
(HCI)
• Technologies should bring
people joy, not frustration
• Design principles and
methodologies have been
developed over years
• More important,
evaluation and validation
criteria have emerged

HCI method: user-centered
design
• Ensure users can use a tool
effectively
• Put users and their tasks at the
center of the design process
• We can avoid losing the focus on
our application development by
starting with the users’ concerns
http://usability.msu.edu/

HCI method: prototypes and cont.
evaluation
• Help understand what users want early on,
determine if design is effective
• Build mock-ups and low-fidelity prototypes early
on, evaluate them empirically
• Users may not know what they want until they see
it; integrating them in the design requires
communicating effectively; we also need to know
how are we going to measure.

Outline
• Introduction
• DM is not the same as DM

Applied Crisis
Computing Example
to Assist Coordination:
Donations Matching

Thanks, But No Thanks …
• Many people want to
donate during disasters
• Waste occurs due to
resources being over- or
under-supplied
• Goal: understanding
what is needed and what
is offered by social media
users
http://www.npr.org/2013/01/09/168946170/thanks-but-no-thanks-when-post-disaster-donations-overwhelm

Matching requests with offers
How to volunteer, donate to Hurricane
Sandy: <URL>
If you have clothes to donate to those who
are victims of Hurricane Sandy …
Red Cross is urging blood donations to
support those affected <URL>
I have TONS of cute shoes & purses I want
to donate to hurricane victims …
Does anyone know how to donate clothes
to hurricane #Sandy victims?
Does anyone know of community service
organizations to volunteer to help out?
Needs to get something, suggests scarcity:
REQUEST (demand)
Offers or wants to give, suggests abundance:
OFFER (supply)

RT @OpOKRelief:
Southgate Baptist
Church on 4th Street in
Moore has food, water,
clothes, diapers, toys,
and more. If you can't
go,call 794
Text "FOOD" to
32333, REDCROSS to
90999, or STORM to
80888 to donate $10
in storm relief.
#moore #oklahoma
#disasterrelief
#donate
Want to help animals in
#Oklahoma? @ASPCA
tells how you can help:
http://t.co/mt8l9PwzmO
CITIZEN SENSORS
RESPONSE TEAMS
(including humanitarian
org. & ‘pseudo’ responders)
VICTIM SITE
Coordination
of
needs and
offers
Using Social
Media
Does anyone
know where to
send a check to
donate to the
tornado
victims?
Where do I go to
help out for
volunteer work
around Moore?
Anyone know?
Anyone know
where to donate
to help the
animals from the
Oklahoma
disaster? #oklaho
ma #dogs
Matched
Matched
Matched
Serving the need!
If you would like to
volunteer today, help is
desperately needed in
Shawnee. Call 273-5331
for more info

A supervised learning approach

Information extraction: core &
facets
• Core of the phrase is the “what”
• Other facets may include “who”, “where”, “when”, etc.
Rotary collecting clothing and other donations in New Jersey <URL>
{ source: “Twitter”, author: “@NN”, text: “Rotary collecting clothing and other
donations in New Jersey <URL>”, donation-info: { donation-type: “Request”, donation-
type-confidence: 0.8, donation-organization: “Rotary”,
donation-item: “clothing and other donations”, donation-location: “New Jersey” }, … }

Some example matches [naïve
method]
• Pair 1:
• Anyone know of volunteer opportunities for hurricane Sandy?
Would like to try and help in anyway possible (OFFER)
• RT @Gothamist: How To Volunteer, Donate To Help Hurricane
Sandy Victims http://t.co/fXUOnzJe (REQUEST)
• Pair 2:
• I want to send some clothes for hurricane relief (OFFER)
• Me and @CeceVancePR are coordinating a clothing/food drive for
families affected by Hurricane Sandy. If you would like to donate,
DM us. (REQUEST)

Much work remains to be done
• Matching quality depends on type of donation
• Improvements on item representation are necessary
• Sparsity is part of the problem
• Improvements on matching quality are necessary
• Hybrid approach needs to be investigated
• Budget of K crowdsourcing calls, which items to annotate?
• A real-world system should use continuous querying, is this
efficient?
Similar approach is applicable in other problem
contexts of coordination as well!

Objective: Support
Decision Making and
Coordination of
Actions

An analogy: product comparison
sites
• What product comparison sites do
today
• Collect pieces of information having
diverse structure (each site has its own)
• Enrich them with automatically-extracted
facets (photo, name, reviews, etc.)
• Cluster/de-duplicate
• Enable search by extracted facets
• In our case, there is almost no structure
to start with, just context
http://pricegrabber.com/

First: extract facets from
unstructured text
• Collect messages
• Classify according to several ontologies
• Not only content classification
• Also Author/Source classification: discover roles
• Extract core aspects / information nuggets
• Identify the key portion of a message
• Extract facets
• Geo-locate

Second: manage data and enable
faceted retrieval
• Support real-time insertions
• Must be visible immediately
• Support real-time updates
• E.g. new user assessments/labels of data
• E.g. new parameters for an automatic classifier
• Support complex queries
• Faceted retrieval on complex predicates
• Return relevant results
• Relevance is based on multiple signals (geo, time, IR-based, etc.)

Third: discover relationships and
clusters
• Clustering
• Near-duplicate detection
• Same event/story/etc.
• Data-driven geographical
regions
• Discover relationships
• Content-Reply ?
• Claim-Refutation ?
• Etc.
• Best supported by linked data
management systems
http://www.jaunted.com/
At a high level, what are the names of
the touristic hot-spots of the word?

Fourth: enable high-level
operations
• Summarization [static]
• Synthesize/extract a high-level description from a set of items
• Semantic clustering [static]
• Determine clusters based on high-level characteristics of data
• Event detection [dynamic]
• Discover large changes in the data at some level of abstraction
• Topic tracking [dynamic]
• Discover how a topic (an aspect of the data) evolves over time

Focus on decision making and
coordination
• Do not start by thinking on data visualization
• Data visualization is constrained by the richness of your data
• Start by thinking on how to make your data richer
• Key questions to prioritize R&D on these systems:
• Who will consume the data?
• What decisions does this person or this community need to take?
• Which aspects of the data support these decisions?
• How do we know the decision was correct?
• Can the end-users of the social media analysis make better decisions
than the non-users?

Example questions during
decision-making by actors
(a.) Seeker/Demander
• Whom to follow
(provider)
• Where to find resource
info
• Whom to contact in the
Responder teams
(b.) Provider/Supplier
• Whom to follow (Seeker)
scarcity info
• Whom to inform in the
Responder side
(c.) Responder
• Whom (seeker/provider)
to contact/DM/Mention
scarcity/availability info.
• Whom to communicate
to deliver the right info.
in right time

Data availability: chicken-or-egg
problem
http://www.vtaide.com/
People’s posts don’t
include some data
Because nobody is
looking for that data

The semantic gap
• Introduced ca. 1989 in the context of multimedia retrieval
• Low-level features are far from high-level information needs
http://www.semanticmetadata.net/

• Vertical operators
facilitate
transcending from
data-information-
knowledge-wisdom
using background
knowledge
• Horizontal operators
facilitate semantic
integration of
multimodal
observations
Analogy for Low level Data to
High Level transformation
http://www.slideshare.net/apsheth/physical-cyber-social-computing-an-
early-21st-century-approach-to-computing-for-human-experience

Semantic gap: ML/DM/NLP/IR/…
• Automatic methods for classifying and extracting information
from short pieces of text are usable but from perfect
• Noisy texts make the problem harder
• Social media English is a particular dialect of English
• Short texts make the problem harder
• There is not enough context to disambiguate
• Frequency-based methods to determine key words are not usable
• Important subtleties escape us
• e.g. irony in sentiment analysis

Intentions: chicken-or-egg
problem
http://www.vtaide.com/
Some types of
coordination do not
often happen online
Because there are no
platforms supporting
such coordinations

Fine-grained analysis of intentions
• People go online during disasters for a variety of reasons
• How good is our understanding of these reasons
• Suppose we know the top-3 reasons, how many people those
reasons cover
• The only way of operating with a long-tail of information needs
is to think in the more general terms possible
• Plus opportunistically creating “vertical” systems for niche needs

Towards a generic crisis response
ontology
• UN effort on generic ontology (taxonomy and relationships)
• HXL (Humanitarian Exchange Language)
• Still a gap between what has been modeled so far vs. what can
be used (supported via data and analytics)
• Current efforts in the W3C community on ‘Emergency
Information management’ on extending HXL with other
existing relevant ontologies and create a necessary and
sufficient model
More about HXL: http://hxl.humanitarianresponse.info/ns/index.html

Continuously-evolving models
• How do we capture the existing knowledge evolving around an
event Moore is a suburb
of Oklahoma City
If you would like to volunteer
today, help is desperately
needed in Shawnee. Call 273-
5331 for more info
Shawnee is a suburb
near Moore
Geographies:
Shawnee
Moore
Focus areas for
Data collection,
processing and
analytics

Outline
• Introduction
• New systems focused on actions and coordination

Principle 1: Explicitly identify
target users
• This may not be a homogeneous groups
• Identify profiles
• Background, skills, etc.

Target users: examples
• Headquarters Humanitarians
• Policy, Information Products, Coordination
• Field Humanitarians
• Logistics, Relief, Coordination
• Digital Humanitarians
• Information Collection
• Analysis

Principle 2: Engage users in co-
design
• Do not let them offload requirements and then leave
• We want them to co-design with us
• This requires effective tools for communication
• e.g. wireframe designs, user stories, etc.

Principle 3: Socio-technical
systems
• Conceptualize the system as hybrid (human and computer
intelligence) from the beginning
• Improve response in a continuous fashion
• We want users to be part of the operation of the systems
themselves

Principle 4: Empirical evaluation
through actions
• We want systems that look good and are easy to use
• We do not evaluate based on looks
• Are the actions of users better than those of non-users?

There is a part for
everybody in this
community
Hackers, scientists, humanitarians, everybody.

Hackers
• Create and curate useful datasets
• Create dataset remixes
• Create software tools
• Create libraries
• Create interoperability

Computer scientists
• There are many open problems in ML, DM, NLP, etc.
• Collaborating and partnering in humanitarian computing
• It is easier to share data and solutions in this application domain
than in commercially-driven ones
• This is also a rich test bed for testing algorithms
• Those algorithms can be useful well beyond humanitarian
computing

Social scientists
• There are many open questions about how, why, people
coordinate, how to motivate them, what information do they
require, how to present that information, etc.
• Which organizational structures are better during different
phases of crisis- mitigation, rescue, relief, recovery and rebuild
• Humanitarian and crisis computing projects need to be
assessed and evaluated for intended impact.
• How to communicate these projects-- need to be
communicated in non-technical language that humanitarian
policy makers understand.

Humanitarian organizations
• It takes two to tango!
• Your scientific partners are not
providers/vendors
• Scientists want access to your experts and data
• Access to experts and problems is extremely
important
• This is a win-win situation: help us create
partnerships
http://worcestertango.org/

Everybody
• Interdisciplinary research is not easy to execute
• But an unidirectional approach will create only more gaps in
the research-to-practice pipeline.

Thanks to
• Nation Science Foundation (NSF) for SoCS project grant: Social Media Enhanced Organizational
Sensemaking in Emergency Response
• Kno.e.sis Twitris team, Prof. Valerie Shalin, Prof. John Flach, Andrew Hampton in the Dept. of
Psychology (Wright State U)
• Prof. Srini Parathasarathy, Yiye Ruan, Dave Fuhry (Ohio State U)
• Fernando Diaz, Microsoft Research
• Shady Elbaussoni, Beirut University
• Muhammad Imran, QCRI
• Jakob Rogstadius, Madeira University
• Our colleagues for suggestions on the material including Sahana project @UMD and ISI @USC, etc.
• Images used here belong to their respective owners, we are grateful to such usefulness of their work
that these images can be illustrative in certain contexts! Many thanks!

Questions,
Discussion and
Feedback
• References and reading material:
• http://www.knoesis.org/hemant/present/icwsm2013
• http://humanitariancomp.referata.com/
• Got Questions? – Talk to us on Twitter: @hemant_pt , @ChaToX , @PatrickMeier , @amit_p

Crisis Mapping, Citizen Sensing and Social Media Analytics: Leveraging Citizen Roles for Crisis Response Coordination

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Crisis Mapping, Citizen Sensing and Social Media Analytics: Leveraging Citizen Roles for Crisis Response Coordination

Similar to Crisis Mapping, Citizen Sensing and Social Media Analytics: Leveraging Citizen Roles for Crisis Response Coordination (20)

Recently uploaded

Recently uploaded (20)

Crisis Mapping, Citizen Sensing and Social Media Analytics: Leveraging Citizen Roles for Crisis Response Coordination

Editor's Notes