WeGov Analysis Tools toconnect Policy Makers with     Citizens Online   Timo Wandhöfer, GESIS tGov 2012, Brunel University
WeGov Project• Where eGovernment meets the eSociety• Seventh framework programme ICT for  “Governance and Policy Modelling...
Stakeholder                        Engagement• Prototype 2.5  – EU Parliament  – German Bundestag  – State Parliament NRW ...
Real-Life Example                                                  • What are citizens‘ opinions  Key QuestionsNuclear was...
Functional                        Requirements• Search SNS  – General query-based searches  – Multiple SNS  – Long term au...
Toolbox• Toolbox is as it says: a box of tools• Box is the infrastructure  – Contains tools  – Link tools together to make...
Toolbox Concept1 – SNS search•   Local•   Global                                        3 - Results2 – WeGov Analysis     ...
Topics and Opinions   Characteristics   • Identifies groups of      words (topics) that arise      in a wider debate   • E...
Frequency of Posts     Characteristics     • Shows concentration        of tweets in the        concerned time        peri...
Prediction of     Discussion ActivityCharacteristics• Predict which posts (top posts) are   expected to generate more   at...
Modeling User      BehaviorCharacteristics• Classify users according to their   behavior and interactions within SNS• Usin...
To be continued…Final toolbox
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WeGov Analysis Tools to connect Policy Makers with Citizens Online

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WeGov Analysis Tools to connect Policy Makers with Citizens Online

  1. 1. WeGov Analysis Tools toconnect Policy Makers with Citizens Online Timo Wandhöfer, GESIS tGov 2012, Brunel University
  2. 2. WeGov Project• Where eGovernment meets the eSociety• Seventh framework programme ICT for “Governance and Policy Modelling “• 7 partner: UK, Belgium, Greece, Germany• Jan 2010 – Sep 2012• Evaluation of prototype 2.5• Validation of analysis components• Final toolbox version due Sep 2012
  3. 3. Stakeholder Engagement• Prototype 2.5 – EU Parliament – German Bundestag – State Parliament NRW (Germany) – Cities (e.g. Ghent) – Parties• Scheduled – States (e.g. state chancellery) – Media (Newspapers, Radio Channel) – Organisations / NGOs
  4. 4. Real-Life Example • What are citizens‘ opinions Key QuestionsNuclear waste deposit (sentiments) on Gorleben? • What do people say that liveGorleben (Germany) Policy Maker • What people are talking about? near to Gorleben? • What do people say within • What their opinions are? the constituency? • How strong is the influence • WeGov people? like Fukushima? Who are the influential of events • Is the topic Gorleben getting • Where on SNS are the debates happening? Source: http://goo.gl/qVhmz “hotter” or “colder”? • Who to engage for dialogue, information , dissemination? Citizens • What do people think on similar topics – nuclear energy, phase-out?
  5. 5. Functional Requirements• Search SNS – General query-based searches – Multiple SNS – Long term automatic repeated searches – Focus on a geographical area• Analyse search results – Summaries of discussion themes and opinions – Recommendations of which SNS users to follow – Current hot topics
  6. 6. Toolbox• Toolbox is as it says: a box of tools• Box is the infrastructure – Contains tools – Link tools together to make composite tools – Modular: further tools can be added as they become available• Tools do things – Intended to be flexible and applicable to many different use cases
  7. 7. Toolbox Concept1 – SNS search• Local• Global 3 - Results2 – WeGov Analysis • Dialogue with citizens• Topics and Opinions • Public relation• Frequency of Posts • Press work• Prediction of Discussion Activity• Modeling User Behavior
  8. 8. Topics and Opinions Characteristics • Identifies groups of words (topics) that arise in a wider debate • Each topic has key users / key posts • Uses content related factors (word frequency, hash tags)
  9. 9. Frequency of Posts Characteristics • Shows concentration of tweets in the concerned time period • Example is on search term “European citizen initiative” • 62 posts with activity peaking on first day
  10. 10. Prediction of Discussion ActivityCharacteristics• Predict which posts (top posts) are expected to generate more attention• Content of post is important • Not written in the afternoon • Written in a familiar language • Written by people who follow many users and listen… • Statement states to be negative• Top users generate more top posts
  11. 11. Modeling User BehaviorCharacteristics• Classify users according to their behavior and interactions within SNS• Using user features • Number of followers / people to follow • Number of posts • Length of use • Frequency of posts• Groups: Broadcaster, information source, daily user, information seeker, rare poster• Most influential and engaged role is information source
  12. 12. To be continued…Final toolbox
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