Part 1 - TU Delft

29/01/15
The Science of
Social Data
Geert-Jan Houben
TU Delft
Web Information Systems
&
Delft Data Science
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Intelligent
typically information technology,
computing science,
and a natural focus on software
Intelligent Cities
the complexity is thought to be
in efficiency
a prescriptive design approach:
closed, fixed, centralized
data representing the ‘world’ is
made to fit the software
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Web & Data
the Web brought linking data
& connecting it to people for
inclusion & adaptation: utility
a descriptive approach:
open, dynamic, decentralized
an unprecedented source
of data about the ‘world’
(that people are part of) “big data, too big to handle”
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Data Science
technology to handle big data
asks for a fundamentally
new computing science
digital (Web) data
and its descriptions of the world
bring a new complexity
to make sense of the data, data
science is all about Semantics,
w. Scale, Speed & Sustainability
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Data
Gap
Computational
power
Time
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Societal challenge of Social Data
unlocking data generated by
humans and their take on the
world, typically with
yet-to-discover semantics
enabling the effective use of the
data for organizations that aim to
include and serve
large numbers of human users
win-win for customers, patients,
travelers, students, etc.
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Scientific challenge of Social Data
1) understanding properties of
human-generated data
2) creating technology &
systems to make sense of data
the Web is the largest humanmade artifact ever made and a
synergy of technology & humans
– scientists & engineers should
study fundamentals as well as
technology in deployment: utility
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Social Data:
From the people
For the people
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Science of Social Data:
Synergy of Technology & Humans
Fundamental & Experimental
Utility
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1 - It’s data ‘about’ people & world
‘About people & world’
most social web data analytics
is about re-purposing data that
is out there available or
we already have in our systems
main research goal is to go
from low-level features
to meaningful concepts
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Interpretation
Emergencies & incidents
assessing from social web data
what is happening
or about to happen
for public safety
asks for well-designed pipelines
with many small decisions for
producing actionable knowledge
requires extensive real-world
experimentation
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Multiple perspectives
Urban analytics
seeing the city through open
social urban data
for modeling & ‘reality checking’
of city flows
data can tell different stories
from different perspectives
and different semantics
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Adaptation
Online education
analytics to make online education
truly learner-centric and to adapt to
the students & their backgrounds
massive online education is
about massively adapting
to the context of use
with increasing diversity comes
importance of social and cultural
features: inclusion – Nr.1 TopSector
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‘Data Analytics machines’
Next
1) by design from using data out of
manufacturing to generating
data about what people want
2) well understood –
models, languages & metrics
for data analytics to understand
& measure its usage
and deploy it ‘as a machine’
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2 - It is data ‘by’ people
‘By people’
besides re-actively taking what’s
there, people could contribute to
creating digital data –
they know best about their world
main research goal is to
pro-actively stimulate people
to effectively
create and complement data
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Complementing
Social sensing
engaging people
in collecting data
about their living environment
complementing
physical sensing
and ‘official’ data
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Collaborative
Workforce engagement
in the professional environment
where bottom up work floor
knowledge is not included
gamification techniques enable
collaboratively building and
sharing knowledge
that complements
professional resources
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‘Data Creation machines’
Next
1) by design sustainable data creation
specific for the context
2) well understood –
deploy data creation machines
with transparent and
well-understood properties
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3 - Making sense ‘with’ people
‘With people’
data often in language that
humans can understand, while
software machines require
training & tuning
main research goal is to use
human computing for
complementing software
interpretation & making sense
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Crowds & Niches
Crowd annotation
involving general crowds and
qualified niches of domain
experts
for annotating large
collections of (heritage) objects
with domain-specific expertise
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Domain Knowledge
Collaborative
knowledge creation
for forums and ‘question
answering’ platforms where
professionals and learners
seek domain knowledge
technology for interpreting
expertise and experts
enables people themselves to
construct domain knowledge
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‘Human Interpretation machines’
Next
1) by design for sustainable human and
crowd enhanced interpretation
Crowd capacity:
designing a crowd
to produce knowledge
with given properties
2) well understood –
strategies & metrics to
effectively & transparently
deploy ‘human & crowd
interpretation machines’
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The science of sense making systems
research agenda: towards theory and technology for
(software and human-enhanced) machines to create value out of data
Scale
Semantics
how to make sense of social data
with its variety, accuracy, and diversity?
how to handle large volumes of data
and engage large groups of people?
Speed
how to create and interpret in real-time
and in changing contexts?
Sustainability
how to ensure sustained functioning
of people-enhanced systems?
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Unlocking Social Data:
Software & Human-enhanced Machines
with Well-Understood Properties
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Science of social data
1.  Social data gives us one of the largest reflections of the world,
but/and it is a man-made reflection
‘unique opportunity turning into interesting research problem’
2.  Sense & value come from big data, but even more so from what
(software and human-enhanced) machines can make of the data
‘V = M * D’
3.  The power of what machines can do with the data needs to be
well-understood and transparent for solid engineering and uptake
‘what machines can do and what they cannot do’
4.  Science and technology follow the principles of the Web
‘fundamental & experimental’
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