The document discusses the Sense4us toolkit which aims to help policymakers make more informed decisions by analyzing social media, open data sources, and modeling policy problems. It describes the different components of the Sense4us toolkit, including tools for topic analysis of social media, sentiment analysis, cognitive mapping of policy issues, and simulation of policy options. The document also discusses challenges in using social media and open data to inform policymaking and demonstrates how Sense4us addresses these challenges through various case studies and examples.

1. Policy Making in a Complex World: The
Opportunities and Risks Presented by New
Technologies
2nd European TA Conference (PACITA)
Thursday, February 26th 2015
1

2. Session Schedule
• General Introduction
• Analysis of social media to inform policy
making
• Modelling and simulation of public policy
problems
• Case study demonstration of current toolkit
• Technology assessment: evaluation of design
assumptions
2

3. 3
Introduction
Steve Taylor, IT Innovation

4. Contents
• Sense4us overview
– Project objectives and how we address them
• Components of Sense4us
• Example of how Sense4us helps its users
4

5. Information Management Challenges faced by
Policy Makers
• Too much information
– Need to sift through the deluge
of information available today
• Potentially untapped resources of
relevant information online
– Open data, social networks,
forums, local blogs, etc
• “Unknown unknowns”
– There may be relevant
information policy makers are
not aware of
• The full impact of policy is not
always obvious when it is created
– Unexpected outcomes & affected
citizens
5
… and How Sense4us Meets them

6. Sense4us Toolkit Overview
6
Theme
Analysis
Document
Summary &
Keywords
Related
Concepts &
Themes
Linked Open
Data Search
User
Social
Media
Search
Social Media
Comments
Sentiment
Analysis
Opinions &
Sentiments
Model
Builder
Policy
Model
Policy
Simulator
Simulation
Results
Policy
Document
Sense4us tool
Data user can see
Search Terms
& Keywords
UI + Infrastructure

7. Sense4us: An End User Partner’s Perspective
7

8. Case Study
8
Navitus Bay Wind Farm
• Planning Application for
wind turbines in the sea
off the south coast of
the UK
• Highly controversial

9. (Real) Case Study &
(Fictitious) Examples of Data
9
Theme
Analysis
• Location
• proposed energy
benefits
• Current analysis of
pros and cons
• Other wind turbine
installations (& success
or not)
• Strategies to achieve
planning success
• Concerns (impact on
tourism & nature)
• Benefits (green energy)
Linked Open
Data Search
Search Terms
& Keywords
User
Social
Media
Search
Sentiment
Analysis
• Strong negative sentiment
from local residents
• Positive reaction from green
campaigners
Model
Builder
• Relationship
between turbine
location and tourism
revenue
Policy
Simulator
• If turbines are 10 miles
from the coastline,
tourism revenue will be
hit by 20%
Planning Application
Document
Key Factors for model:
• Turbine size
• Turbine location
• Tourism Revenue
• Unemployment
• Reduction in local area‘s
fossil fuel usage
• Resistance from local
communities
• “Disgrace!”
• “Good source
of energy”
• “It will ruin
the view”
• “Will my fuel
bills be
cheaper?”

10. Project Status
• Month 17 of 36
• Initial engagement with end users complete
• Initial prototype complete
• Plans to demonstrate prototype to end users
in next months
– Gather feedback
– Update design
– Update prototype
10

11. 11
PART 1
Using Social Media To Inform Policy
Making: To whom are we listening?
Miriam Fernandez, Open University
Harith Alani, Open University

12. Introduction
• Social media
– A revolutionary opportunity for governments to
learn about the citizens and to engage with them
more effectively but,
– When using social media to inform Policy Making…
• To whom are we listening?
• What are the key policy subject areas of discussion?
• How do users feel about those policy subject areas?
12

13. Who is talking about policy in social
media? (I)
13
• Three main lines of work
– Statistics about the citizens’ participation on ePlatforms
• Not social media participation
– Statistics about users participating in social media
• Not narrowed to eParticipation / political discussions
– Studies of political discussions in social media
• In the context of political events (elections, revolutions, etc.), not
focused on relevant topics for policy making
Participation in
policy making
Participation
in social media

14. Who is talking about policy in social
media? (II)
• Goal:
– Study the characteristics of those users discussing policy in social
media and the key topics and sentiment of their discussions
– Data
• Policy topics: 76 policy topics collected from 16 PMs, all from different
institutions in Germany
• Generic topics (such as women) were filtered to avoid collecting noisy data ->
42 remaining topics
– Betreuungsgeld (Care Benefit )
– Bildungspolitik (Education Policy)
– Bürgerrechte (Civil Rights)
• Topics were monitored in Twitter for a week.
– The developed algorithms are designed to track discussion dynamics over time
14
Fernandez, M., Wandhoefer, T., Allen, B., Cano, E., Alani, H.
Using Social Media To Inform Policy Making: To whom are we listening?
European Conference of Social Media (ECSM 2014)
Init Date Final Date Num Posts Num Uses
04/01/2014 12/01/2014 17,790 8,296

15. Who is talking about policy in social
media? (III)
• Top Contributors
– Less than 6% of the users are responsible of 36% of the generated content
– 73.4% of top-contributors are NOT citizens but news agencies and other organisations
• The Average Contributor
– Is more active, popular and engaged than the average Twitter User
• Geographic Distribution of Users
– Higher concentration of users occurs in constituencies of high population density
– Users engaged in social media conversations around policy topics tend to be geographically
concentrated in the same regions than users engaged in eParticipation platforms
15
(a) Distribution of eParticipation projects (b) Distribution of Twitter users
Figure 1: (a) Distribution of eParticipation projects in Germany (http://www.politik.de/politik-
de/projekte_entdecken/beteiligungskarte) (b) Distribution of Twiter users: yellow are locations with less than 10
users, pink are locations with 10 to 50 users, red are locations with more than 50 users

16. What are they topics of discussion and
the sentiment around those topics?
• Topic Distribution
– Few topics are extensively discussed during the analysed period
• Privacy, Network Policy, Minimum Wage, Copyright, etc.
– The majority of topics are underrepresented
• Sentiment Distribution
– Top Negative topics
• Genetic Engineering, Immigration, Referendum, European policy and donations to
political parties
– Top Controversial topics
• Privacy, Fracking and Domestic Policy (High percentage of positive and negative posts)
16
0
500
1000
1500
2000
2500
3000
3500
4000
privacy
networkpolicy
minimumwage
copyright
fracking
domesticpolicy
genetic…
resin
migrants
equality
femaleratio
rightwing
referendum
leftwing…
educationand…
energypolicy
europeanpolicy
partydonate
socialpolicy
speedlimit
financialpolicy
nosmoking
caremoney
transportpolicy
generational…
debtbrake
environmental…
npdban
nonsmoking…
sociallyticket

17. SentiCircle: understanding topics and
sentiment around political discussions (I)
17
Helps summarising policy
discussions
• A model for extracting the facts
(aspects) of a given topic in a tweet
collection
• Classifies public opinion on these
facts (aspects) as “in-favour” or
“not-in-favour” for studied topic
Environment ROI
Efficiency
Weather
Installment
Maintenance
Funding
Noise
Renewable Energy
In-Favor
Not-In-Favor
Evidence Representation of the topic
“Renewable Energy” using
SentiCircles
Saif, H., Fernandez, M., He, H., Alani, H. SentiCircles for Contextual and Conceptual Semantic
Sentiment analysis of Twitter. European Semantic Web Conference (ESWC 2014)
Saif, H., He, Y., Fernandez, M. and Alani, H. (2014) Adapting Sentiment Lexicons using
Contextual Semantics for Sentiment Analysis of Twitter, Workshop: Semantic Sentiment
Analysis, Crete, Greece. BEST PAPER AWARD!
More Less

18. SentiCirle: understanding topics and
sentiment around political discussions (II)
• SentiCircle: Lexical-based sentiment
representation model
– Assigns sentiment to a term by considering its co-
occurrence patterns with other terms
18
• The radio T-DOC is computed based on the degree
of correlation between the two terms
• The angle is computed based on the prior
sentiment of the term, extracted from an existing
lexicon

19. Discussions
• Understanding who are the users discussing policy in social media
and how policy topics are debated could help PMs assessing how
the citizen’s views and opinions should be weighted and considered
to inform policy making
• This research is being incorporated into the Sense4us toolkit
• Several problems arise when using social media for this purpose:
– Data is distributed in multiple social platforms
– More research is needed to understand how representative is the
subset of the population discussing policy in social media
– Social media -> big data issues: volume, variety, velocity and veracity
of the data
19

20. 20
PART 2
Modelling and Simulation of Public
Policy Problems – Sense4us Model
Builder and Simulation Tool
Aron Larsson,
Osama Ibrahim,
Anton Talantsev,
eGovlab Department of Computer and Systems Sciences (DSV)
Stockholm University

21. Policymaking process model
21
Prescriptive analysis (Impact Assessment):
Carried out at the early stages of policy
development), which encompasses the
forecasting of consequences and
prescriptions about which policies should
be implemented.
Retrospective analysis (Evaluation):
Tries to understand the causes and
consequences of policies after they
have been implemented.

22. Decision support framework approach
22
Enabling policy analysis and decision evaluation where problem structuring is supported by linked open
data, topic analysis and sentiment analysis of social media data.
Policy problem structuring and
modelling
(Causal/cognitive map)
Design policy options, consequence
assessment, generate options
(Simulation)
Decision evaluation of policy options
(Decision analysis)
Sentimentanalysis
Evidenceextractionfromopendatasources

23. Why problem structuring?
23
• Searching for the right information
• Capture a policy maker’s views about a problem
• Understanding decision making context and communication
of problem understanding
• Structuring more complex cause-effect relationships
• Identifying where and how interventions have impact
• Enabling for decision evaluation of policy options

24. Objectives
Cognitive/causal mapping
24
CO2
emissions
Means
Subsidy
bus
tickets
-
Hmm…??

25. Cognitive/causal mapping
25
CO2
emissions
Means
Subsidy
bus
tickets
-
Car traffic
+
-
Objectives
Hmm…??
LOD LOD

26. Cognitive/causal mapping
26
CO2
emissions
Means
Subsidy
bus
tickets
-
Car traffic
+
Objectives
Member of
council

27. Cognitive/causal mapping
27
CO2
emissions
Means
Subsidy
bus
tickets
-
Car traffic
+
Objectives
”It cannot cost more than €35 a
month”
”It’s not the price, it is the
frequency of buses”
Member of
council

28. Cognitive/causal mapping
28
CO2
emissions
Means
Subsidy
bus
tickets
-
Car traffic
+
Objectives
Increase bus
frequencies
-
-
Member of
council

29. Cognitive/causal mapping
29
CO2
emissions
Means
Subsidy
bus
tickets
Car traffic
+
Objectives
Increase bus
frequencies
-
-
Member of
council
Traffic planner

30. Cognitive/causal mapping
30
CO2
emissions
Means
Subsidy
bus
tickets
Car traffic
+
Objectives
Increase bus
frequencies
-
-
Traffic planner
Gas driven
buses
-
Bus company
CEO
Member of
council

31. Cognitive/causal mapping
31
CO2
emissions
Means
Subsidy
bus
tickets
Car traffic
+
Objectives
Increase bus
frequencies
-
-
Gas driven
buses
-
Town
commerce
+
-20%
+5%
+
Stakeholder
Traffic planner
Member of
council
Bus company
CEO

32. Evaluation concept:
Impact potency
32
CO2
emissions
Means
Subsidy
bus
tickets
2
Car traffic
+
Objectives
Increase bus
frequencies
2
-
-
Gas driven
buses
1
-
Town
commerce
+
-20%
+5%
+
Stakeholder
Bus company
CEO
Traffic planner
Member of
council

33. Evaluation concept:
Forward analysis
33
CO2
emissions
Means
Subsidy
bus
tickets
Car traffic
Objectives
Increase bus
frequencies
-0.2
1m
-0.5
1m
Gas driven
buses
Town
commerce
-20%
+5%
Stakeholder
-1.0
0m
+1.0
0m
+1.2
0m
+0.6
4m
Traffic planner
Bus company
CEO
Member of
council

34. Forward analysis
34
CO2
emissions
Means
Subsidy
bus
tickets
Car traffic
Objectives
Increase bus
frequencies
-0.2
1m
-0.5
1m
Gas driven
buses
Town
commerce
-20%
+5%
Stakeholder
-1.0
0m
+1.0
0m
+1.2
0m
+0.6
4m
20%,
t=0
-4%,
t=1
7%,
t=4
25%,
t=12
-29%,
t=12
Traffic planner
Bus company
CEO
Member of
council

35. Group decision and game concepts
35
CO2
emissions
Means
Subsidy
bus
tickets
Car traffic
Objectives
Increase bus
frequencies
-0.2
1m
-0.5
1m
Gas driven
buses
Town
commerce
-20%
+5%
Stakeholder
-1.0
0m
+1.0
0m
+1.2
0m
+0.6
4m
20%,
t=0
-4%,
t=1
7%,
t=4
25%,
t=12
-29%,
t=12
Traffic planner
Bus company
CEO
Member of
council

36. Group decision and game concepts
36
CO2
emissions
Means
Subsidy
bus
tickets
Car traffic
Objectives
Increase bus
frequencies
-0.2
1m
-0.5
1m
Gas driven
buses
Town
commerce
-20%
+5%
Stakeholder
-1.0
0m
+1.0
0m
+1.2
0m
+0.6
4m
15%,
t=0
5%,
t=4
15%,
t=12
-21%,
t=1210%,
t=6
Bus company
CEO
Traffic planner
Member of
council

37. To sum up
We are building a tool that assist public decision processes through
modelling a public policy problem, simulating policy consequences
for decision evaluation considering multiple objectives and
stakeholders.
Visually structure the policy problem for increased problem
understanding
– Show an understanding of the relations between policy
instruments (funds, taxes, subsidies, prohibition, etc.) and
societal effects.
– Generate different means or combinations of means
controlled by different actors to reach similar targets
(scenario generation)
• ”If we change this policy instrument, according to what we know, what are
the effects (over time) on the factors subject to policy targets? Which
stakeholders are affected and how? Will they react and if so what is the effect
on the factors?”
37

38. Contact
Aron Larsson
aron@dsv.su.se
38

39. 39
PART 3
Finding and Analysing Online Data to
Support Governmental Decision Making
Processes - the case of Sense4us
Timo Wandhöfer, GESIS
Max Bashevoy, IT Innovation

40. Persona
• UK Decision maker
• Member of the House of Commons
• Political interest: renewable energy
40

41. Use Case
41
Similarities?
Dissimilarities?
Sense4us
Topics Topics
Debates in the plenum,
draft bills,
press releases
Sense4us
?

42. 42
PART 4
Assumptions to Artefacts:
Understanding the Design Choices
Underpinning the Sense4Us Project
Somya Joshi,
eGovlab Department of Computer and Systems Sciences (DSV)
Stockholm University

43. Objective
• To summarise where we are
• To understand how we got
here
• To visualise where we are
heading
• Where does Technology
Assessment fit into this?
43

44. Where we are at
Milestones
• End user engagement – first
leg (Policy makers’ needs &
requirements)
• Demo of tool – stage one
• Integration of tools
44

45. Understanding how we got here
Design choices & assumptions
• Who is this for?
• What are we hoping to
impact? Where is the
innovation/ added value?
• How will end users engage
with our tool set?
45

46. What the end users expressed
Requirements
• Provenance of data
• Transparency
• Sentiments & Opinions
elicitation
• Summary & Visualisation of
raw data sets
• Localisation of data
• Customisability of tool set
46

47. Assumptions of Technology Impact
• “Transparency in policy making”
• “Policy makers who want to take on board
citizen opinions, discussions, information via
social media resources”
• “Relevance & Provenance of data”
• “Publishers who want to make data accessible
to others as well as increase their own
knowledge ”

48. Assumptions – Impacts & Innovation
• Streamline & improve quality
of linked open data searches
• Citizen solutions &
knowledge will be
summarised & analysed
along sentiment/semantic
lines
• Integration of problem
structuring; support for
impact assessment;
preference elicitation when
there are conflicting goals
48

49. What we hope to achieve
• Co-evolution of tool set in line
with end user feedback
• Integration of problem
structuring and policy analysis
tools
• Extend & deepen knowledge
via Linked Open Data
• Identification of important
stakeholders/ actors
• Reduced cognitive loads on
policy makers: Making sense of
the noise (reducing complexity)
49

50. The road ahead
• Technological integration of
the various components
within the tool set
• User interface – how will end
users visualise, make sense &
engage with our tool?
• Transparency in design (no
black boxes), data relevance
/ provenance (trust) and rich
results
50

51. • Gov 2.0: Enhanced policy decision support
• Social media and linked open data as a rich source of opinions,
preferences, knowledge, that will be harnessed
• Simulation and modeling of policy alternatives & impacts
51
Where we envision we’re heading

52. The contextual landscape
The “vending machine” model of
Governance: where the citizens
only engage with ‘shaking up’ the
system when it doesn’t work
To “Governance as Platform”
Where the platform is a metaphor
for multi-layer decision making in
complex, evolving environments
52

53. Technology Assessment & Political Myths
• Are we designing a Political construct or a
Technological artefact?
• Participatory Design within Policy context?
Stage two of our end user engagement will
further test this concept of designing a tool
set in line with end user feedback
• Collaborative approaches have been argued
to secure legitimacy . What are the
anticipated risks and apprehensions on the
part of end users within the Sense4us
project? E.g. “Political vs. Scientific Fact”

54. Technology Assessment of the
Sense4Us Toolkit
54

55. Discussion on our proposed approach
• How does one demonstrate new concepts to end
users?
• How does one integrate that feedback into future
iterations of the design?
• Our proposed approach is to demonstrate the
various components using examples that are
relevant and easy to understand
• To learn if this is understandable, useful, relevant
and what they would like to see done differently
55

56. THANK YOU!
56