Crisis Mapping, Citizen Sensing and Social Media Analytics

Hemant Purohit Amit Sheth Carlos Castillo Patrick Meier

The Ohio Center of Excellence inKnowledge-enabled Computing (Kno.e.sis)

Wright State, USA

Qatar ComputingResearch 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

7.0 Magnitude Earthquake

Motivation - P

Ushahidi Map for Haiti

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

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

Citizen Sensing

Jakarta twitter map

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

FEMA Task Force Haiti

evolution on Twitter

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

Crisis Response Coordination

UN Clustersystem

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

• Gaps & Challenges• Scale, velocity, redundancy, heterogeneity, bias, noise & verifiability

• Role of Computer Science

• Applied Crisis Computing

• Design Principles

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 ofdamage?

What emergency-responders

want

What computer

scientists can provide

What is supported by current social

media data

Puzzle of Crisis Informatics

• What computer scientists can provide?• Algorithms to detect and predict abnormal

trends

• Semantic abstraction and summarizationof 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

Puzzle of Crisis Informatics

• 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 UCIrvine

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

ToolVisual Geo

Mapping

HumanInputs

Real-time

Update

People to engage

with

Topicalsummary

Exploredata

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 (Organization registry)

Tools: Sahana (Requests List)

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: CrowdMap (Reports)

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 (Topical nugget summary)

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-time information for

needs)

Clicking on a tag brings contextual information– relevant tweets,

news/blogs, and Wikipedia articles

Tools Demo: Twitris (Influencers to engage with, for specific

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 (In R&D: Engagement Interface for

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 toolsfor Disaster Response Coordination

Where can I go for volunteering at my location?

How and Where can one donate?

Challenges

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 Comscorevia 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 2012http://edelman.com/trust

Outline

• Introduction

• Gaps & Challenges

• Role of Computer Science• IR, DM, ML, NLP, SN, HCI

• Applied Crisis Computing

• Design Principles

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• What does it do?• 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

• How can it help us?• 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• What does it do?• Group search results in order to better scan

them; can be done on a query-dependent or query-independent way

• How does it work?• One way is to do a weighted dot product in

which weights are associated to how informative (~rare) are words

• How can it help us?• Makes it easier to deal with large, redundant

collections of texthttp://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

Yan Huang, UNT.edu

DM Method: burst detection • What does it do?• Reliably identifies anomalies in a time series

(e.g. volume of tweets w/hashtag vs time)

• How does it work?• Look for increases above the norm; look for

change patterns that precede crisis

• In general it is hard over noisy signals

• How can it help us?• 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• What does it do?• Track the relative

popularity of different topics over time

• How does it work?• Cluster documents per

time slice, merge across times slices

• How can it help us?• See emerging stories, track

new developments, sub-stories, etc.

TextFlow

DM Method: dimensionality reduction • What does it do?• Represent complex data in simpler

terms

• How does it work?• Find independent pieces of

information, discard/merge correlated ones

• How can it help us?• 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’ axisZ 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 • What does it do?• Learn to separate different classes

of elements, given (relatively) few examples

• How does it work?• Several methods to choose from,

popular ones are SVMs and Decision Trees/Forests

• How can it help us?• Automatic classification of reports

http://www.quora.com/

Example: automatic tweet classification

Caution &Advice

InformationSources

Damage &Casualties

Donations

Health

Shelter

Food

Water

Logistics

...

...

ML method: regression

• What does it do?• The same as supervised

classification but the target is numerical, not categorical

• How does it work?• It learns the parameters of a

function that fits what is observed

• How can it help us?• 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

• What does it do?• Determines classes for tokens or segments

on a text: part-of-speech tags, named entities

• How does it work?• Supervised learning with structured outputs

• How can it help us?• A richer representation of tweets yields

better predictions• Spotting named entities or key phrases can

help summarize tweets

I/prepositioncan/modalsee/verbthe/determinerflames/nounfrom/prepositionhere/adverb

NLP methods: dependency parsing• What does it do?• Identifies relationships between

different parts of a text

• How does it work?• Learned from labeled data using

structured output (output is a parse tree)

• How can it help us?• 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• What does it do?• Connect named entities to concepts, e.g. a

sense on a dictionary or a URL in Wikipedia

• How does it work?• Entities can have multiple senses; the

correct one is picked by using contextual clues

• How can it help us?• 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

3activity, 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. link analysis, network analysis

• 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

• What does it do?• Find communities of densely

connected nodes

• How does it work?• There are many methods,

depending on the definition of community

• How can it help us?• We can identify groups of people

who are closely connected

http://griffsgraphs.com/

GT method: centrality metrics

• What does it do?• 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)

• How does it work?• Pagerank is computed through iterative

calculations over the entire graph

• How can it help us?• 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• What does it do?• Ensure users can use a tool

effectively

• How does it work?• Put users and their tasks at the

center of the design process

• How can it help us?• 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• What does it do?• Help understand what users want early on,

determine if design is effective

• How does it work?• Build mock-ups and low-fidelity prototypes early

on, evaluate them empirically

• How can it help us?• 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

• Gaps & Challenges

• Role of Computer Science

• Applied Crisis Computing• DM is not the same as DM

• Design Principles

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” }, … }

Statistics

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)• Where to find resource

scarcity info• Whom to inform in the

Responder side

(c.) Responder

• Whom (seeker/provider) to contact/DM/Mention

• Where to find resource scarcity/availability info.

• Whom to communicate to deliver the right info. in right time

Open problems

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-wisdomusing 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

• Gaps & Challenges

• Role of Computer Science

• Applied Crisis Computing

• Design Principles• 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

Headquarters

The Field

Digital Humanitarians

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 communityHackers, 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

htt

p:/

/wo

rces

tert

ango

.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