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T he Changing Digital Context of Government

This research program explores the relationship between government and information technology, using various methods such as big data analysis, national surveys, experiments, focus groups, and more. It examines how social media and the internet are transforming society, politics, and the economy worldwide. The study also investigates the impact of tiny acts of participation on collective action and policy change.

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T he Changing Digital Context of Government

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  1. The Changing Digital Context of Government Helen Margetts Professor of Society and the Internet Director, Oxford Internet Institute

  2. Long running programme of research into relationship between government and information technology using mixed methods: ‘big data’/data science analysis, national surveys, experiments, focus groups, elite interviews, web census, user metrics, google analytics 1999 2009 2007 2006 1999 2010 2007 2002 2015

  3. social media = internet platforms that allow users to generate content collective action = activity undertaken by citizens with aim of contributing to public / social goods

  4. Structure1. A Changing Digital World2. A Data Science for Government 3. Inherently Digital Government

  5. Widespread use of the internet and social media is changing society, politics, the economy across the world – people’s lives are intertwined with digital technologies and connections and take place on platforms – FAGAM and beyond A changing (digital) world

  6. The growth of online populations World Bank data

  7. Facebook users across the world Source: Facebook annual reports

  8. Social media allow tiny acts of participation in support of social and political causes

  9. Referral traffic to UK petitions platform

  10. Social information and visibility as drivers of political behaviour

  11. Tiny acts of participation can scale up to huge mobilizations which … ….challenge authoritarian and democratic regimes

  12. …….affect individual lives

  13. …and policy change Tiny acts of participation can scale up – but most mobilizations fail

  14. Successful mobilizations develop fast Petitions receiving more than 10,000 signatures

  15. Success (if it comes) comes quickly YouTube videos show similar pattern (no s-shape) Source: Margetts et al (2015) Political Turbulence: How Social Media Shape Collective Action, Princeton University Press.

  16. Mobilizations against policing in the US Source: Margetts et al (2015) Political Turbulence: How Social Media Shape Collective Action, Princeton University Press.

  17. To block or not to block Trump

  18. Signatures of #Rerun EURef Petition, 2016 Graph: Scott Hale, OII

  19. Collective attention decays very quickly US, UK site outreach Modeling the Rise in Internet-based Petitions T Yasseri, SA Hale, H Margetts - arXiv preprint arXiv:1308.0239, 2013

  20. Leadership without leaders?

  21. The changing context of policy-making See Margetts, John et al (2015) Political Turbulence (Princeton University Press) • Policy-making involves understanding, responding to, changing society • People spend their lives on platforms – eg. social media, governments, NGOs, commercial, sharing economy • And take ‘tiny acts’ of participation in pursuit of policy change • Which can scale up – very quickly • Succeeding without leaders, institutions, collective identity • But most fail • Normal predictors of participation don’t work eg. demographics • Policy-making environment is more unstable, confusing, unpredictable – more turbulent

  22. Act first, identify laterThe end of party identification?

  23. Political turbulence A dynamic system with unpredictable behaviour – high sensitivity to initial conditions, non-linear relationships, high interconnectivity, tipping points … a chaotic system – like the weather Traditional political institutions are creaking at the seams to accommodate turbulence

  24. A data science for politics and governmentDigital interactions generate data for policy-making

  25. ‘Big data’ for public policy • The same phenomenon that brings turbulence – brings new sources of digital data – can we get better at forecasting politics (as well as the weather)? • Large-scale real-time transactional data • Understand citizen’s behaviour, needs, underlying preferences • Allow probabilistic policy making • And government ‘self-improvement’ • And even prediction

  26. Geographical distribution of pro- and anti- immigration petition signers Map: Greg Johnson, Oxford Internet Institute

  27. Understanding institutional changeeg. nature of parliamentary debate Bright, Jonathan (2012), ‘The Dynamics of Parliamentary Discourse in the UK: 1936 – 2011’, paper presented at Internet, Politics and Policy: Big Data, Big Challenges?, September 2012, Oxford Internet Institute OII Research Fellow Jonathan Bright analysed 75 years (12 GB, 740 million words) of parliamentary debates Rate that speakers are interrupted by other MPs has risen over time, suggesting a more contentious political climate

  28. Predicting the Iranian election with Wikipedia Source: Yasseri & Bright, 2013 Preprint arXiv: 312.2818

  29. Can we use social data to inform policy-making and service delivery? • Aim: to observe how people were • searching for, understanding, experiencing • and expressing concern about changes to • UK benefit system (introduction of • Universal Credit) Feasibility study carried out by OII for UK Department of Work and Pensions https://www.gov.uk/government/publications/use-of-social-media-for-research-and-analysis

  30. Searching for universal credit

  31. Viewing Wikipedia

  32. Old and new ‘big’ data • Government transactional data • Government register data • Commercial transactional data • Internet, social media • Tracking data • Satellite data

  33. And new ways of using data • Machine learning • Linking ‘new’ big data with ‘old’ survey data • Visualization • Experimentation • Analysis of large scale complex networks • Probabilistic – predictive - policy-making • ‘Nudging’ political and social behaviour

  34. Experimenting with social information: a natural experiment Introduced March 2012

  35. Social information reinforces (un)popularity ‘Trending petitions on epetitions.direct.gov.uk Investigating Political Participation and Social Information Using Big Data and a Natural Experiment SA Hale, P John, HZ Margetts, T Yasseri– Paper to APSA 2014 Annual Meeting, Washington DC 2014

  36. Challenges to Data Science of Policy-making • Data availability (guarded by internet corporations) • Reputational problems of big data + government • Open data is not big data – big data about people is not open • Skills (policy-makers can lack data science capability) • Need multi-disciplinary cross-sectoral teams • Narrows the gap between research and policy-making

  37. Requiring new kinds of expertiseSocial Data Science @OII • Part of the Alan Turing Institute (ATI): which will place the UK at the forefront of world-wide research in data science • Using data from Twitter, Wikipedia, Zooniverse, Everyday Sexism, elections and petitions data (etc.) to map social structures and interactions • Truly multi-disciplinary environment

  38. Towards essentially digital government

  39. Pressures for digital change and ‘datafication’ of government • Political turbulence • High volume digital information relevant for policy-making generated in society • Major ‘big data’ potential in government itself • Competitive comparisons/expectations • Pressure for digital regulation ofdigital activities • Centralizing networking/control effects Rapid disruptive change in societal behaviours and industrial/economic patterns have become the norm

  40. Government as a Platform • Open standards • Keep it simple • Design for Participation • Experimentation • Data Mining • Learn from Hackers • Lead by Example

  41. Government as a Platform Estonia • Layers (X-Road, eID, eesti.ee) • Simplicity • Openness • (Controlled) participation • Leading by example UK • Legacies • Building blocks (Verify, Notify, GovPay) • www.gov.uk • Data mining • Learning from hackers • Experimentation

  42. Big ethical issues for policy-making • Ethics embedded in the science – think of Turing • Ethical Framework • Deficit (algorithmic transparency, secondary uses) • Consent • Privacy (de-anonymisation; group privacy) • Coverage • Management (eg access, use, audit, reuse, IP) • Data science needs philosophers and ethicists to tackle the awkward questions posed by big data and ‘datafication’ • Ethics of NOT using data

  43. Guiding Principles for Government • Transparency, honesty, fairness, consent, privacy • Innovation, efficiency • Robustness, resilience, adaptivity Complex Tensions and Trade-offs See Christopher Hood (1991) A public management for all seasons Public Administration

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