sense4us pacita event presentation
TRANSCRIPT
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Policy Making in a Complex World: The Opportunities and Risks Presented by New Technologies
2nd European TA Conference (PACITA)Thursday, February 26th 2015
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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
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Introduction
Steve Taylor, IT Innovation
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Contents
• Sense4us overview– Project objectives and how we address them
• Components of Sense4us
• Example of how Sense4us helps its users
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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
… and How Sense4us Meets them
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Sense4us Toolkit Overview
ThemeAnalysis
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 seeSearch Terms & Keywords
UI + Infrastructure
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Sense4us: An End User Partner’s Perspective
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Case StudyNavitus 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
ThemeAnalysis
• 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?”
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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
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PART 1Using Social Media To Inform Policy Making: To whom are we listening?
Miriam Fernandez, Open UniversityHarith Alani, Open University
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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?
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Who is talking about policy in social media? (I)
• 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 ?
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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
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
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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
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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)
0500
1000150020002500300035004000
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SentiCircle: understanding topics and sentiment around political discussions (I)
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
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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
• 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
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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
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PART 2Modelling 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
Policymaking process model21
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.
Decision support framework approach
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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)
Sentiment analysis
Evidence extraction from open data sources
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Why problem structuring?
• 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
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Objectives
Cognitive/causal mapping
CO2 emissions
Means
Subsidybus
tickets
-Hmm…??
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Cognitive/causal mapping
CO2 emissions
Means
Subsidybus
tickets
-
Car traffic
+
-
Objectives
Hmm…??
LOD LOD
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Cognitive/causal mapping
CO2 emissions
Means
Subsidybus
tickets
-
Car traffic
+
Objectives
Member of council
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Cognitive/causal mapping
CO2 emissions
Means
Subsidybus
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
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Cognitive/causal mapping
CO2 emissions
Means
Subsidybus
tickets
-
Car traffic
+
Objectives
Increase bus frequencies
-
-
Member of council
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Cognitive/causal mapping
CO2 emissions
Means
Subsidybus
tickets Car traffic
+
Objectives
Increase bus frequencies
-
-Member ofcouncil
Traffic planner
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Cognitive/causal mapping
CO2 emissions
Means
Subsidybus
tickets Car traffic +
Objectives
Increase bus frequencies
-
-
Traffic plannerGas driven
buses
-
Bus company CEO
Member ofcouncil
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Cognitive/causal mapping
CO2 emissions
Means
Subsidybus
tickets Car traffic +
Objectives
Increase bus frequencies
-
-
Gas driven buses
-
Town commerce
+-20%
+5%
+
Stakeholder
Traffic planner
Member ofcouncil
Bus company CEO
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Evaluation concept: Impact potency
CO2 emissions
Means
Subsidybus
tickets2
Car traffic +
Objectives
Increase bus frequencies
2
-
-
Gas driven buses
1 -
Town commerce
+-20%
+5%
+
Stakeholder
Bus company CEO
Traffic planner
Member ofcouncil
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Evaluation concept:Forward analysis
CO2 emissions
Means
Subsidybus
tickets Car traffic
Objectives
Increase bus frequencies
-0.21m
-0.51m
Gas driven buses
Town commerce
-20%
+5%
Stakeholder
-1.00m
+1.00m
+1.20m
+0.64m
Traffic planner
Bus company CEO
Member ofcouncil
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Forward analysis
CO2 emissions
Means
Subsidybus
tickets Car traffic
Objectives
Increase bus frequencies
-0.21m
-0.51m
Gas driven buses
Town commerce
-20%
+5%
Stakeholder
-1.00m
+1.00m
+1.20m
+0.64m
20%, t=0
-4%, t=1
7%, t=4
25%, t=12
-29%, t=12
Traffic planner
Bus company CEO
Member ofcouncil
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Group decision and game concepts
CO2 emissions
Means
Subsidybus
tickets Car traffic
Objectives
Increase bus frequencies
-0.21m
-0.51m
Gas driven buses
Town commerce
-20%
+5%
Stakeholder
-1.00m
+1.00m
+1.20m
+0.64m
20%, t=0
-4%, t=1
7%, t=4
25%, t=12
-29%, t=12
Unbalanced sc
enarioTraffic planner
Bus company CEO
Member ofcouncil
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Group decision and game concepts
CO2 emissions
Means
Subsidybus
tickets Car traffic
Objectives
Increase bus frequencies
-0.21m
-0.51m
Gas driven buses
Town commerce
-20%
+5%
Stakeholder
-1.00m
+1.00m
+1.20m
+0.64m
15%, t=0 5%,
t=4
15%, t=12
-21%, t=1210%,
t=6
Bus company CEO
Traffic planner
Member ofcouncil
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To sum upWe 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?”
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PART 3Finding and Analysing Online Data to
Support Governmental Decision Making Processes - the case of Sense4us
Timo Wandhöfer, GESISMax Bashevoy, IT Innovation
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Persona
• UK Decision maker• Member of the House of Commons• Political interest: renewable energy
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Use Case
Similarities?Dissimilarities?
Sense4us
Topics Topics
Debates in the plenum,draft bills,
press releases
Sense4us
?
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PART 4Assumptions to Artefacts:
Understanding the Design Choices Underpinning the Sense4Us Project
Somya Joshi,eGovlab Department of Computer and Systems Sciences (DSV)
Stockholm University
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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?
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Where we are at
Milestones• End user engagement – first
leg (Policy makers’ needs & requirements)
• Demo of tool – stage one• Integration of tools
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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?
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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
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 ”
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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
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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)
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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
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• 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
Where we envision we’re heading
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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
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”
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Technology Assessment of the Sense4Us Toolkit
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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
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THANK YOU!