Campaigning by Numbers: The Role of Data-Driven Practices in Civil Society Organisations Amber Macintyre Royal Holloway, University of London Department of Politics and International Relations Submitted for the degree of Doctor of Philosophy in Political Science !"# $orrections submitted 2020 Declaration I, %mber &acintyre, hereby declare that this thesis and the work presented in it is entirely my own. Where I have consulted the work of others, this is always clearly stated. %mber Macintyre 23rd December, 2020 2 Acknowledgements I am incredibly grateful to the amount of people who have believed in me and supported me over the last few years( Due to a few circumstances, I have had the opportunity to work with +ve supervisors over the period, and each has contributed a di,erent perspective to this project( I am grateful for the +rst two years of supervision from %ndy Chadwick, whose knowledge and enthusiasm for the +eld of political communication is inspiring. Chris )atkins, from the Department of Computer Science, provided technical expertise, and a perspective outside of political science that helped clarify my ideas early on. I am grateful to Ben 0’Loughlin for -umping in to be the primary supervisor half way through the project( Ben had already provided important feedback, alongside 2ames Sloam on my advisory panel, both of whom placed my work in a broader literature that I would never have known otherwise( I am grateful for the support from Ursula Hacket who stepped in for the final years and not only brought me energy at a time when I was struggling to see a route forward, but also provided excellent challenges to my theories, consequently strengthening my ability to defend my ideas( 4inally, I could not have come this far without Cristian 5acarri, who supervised the project from start to end. %n expert not only in the +eld, but in the rigour required to achieve high standards of research, Cristian has been vital to my growth as a researcher, and I can only hope to keep learning from his work. I could not have done this PhD without the generosity of 6he Leverhulme 6rust( &y work has also bene+ted from the research environment at Royal Holloway. Particularly the PhD program in the Politics and International Relations Department which was run by Professor. Julia 7allagher, which provided opportunities to discuss research ideas and experiences with fellow PhD students( 6hrough this, I met the %ssorted Cool Kids who it has been an honour to -ourney alongside( 8mily Harding, 6homas )aterton, 9aomi 7rotenhuis and David 5entura have provided solidarity, mutual proofreading, dance breaks, PhD festivals and a countless number of pomodoros together, all of which have brought a lot of pleasure to the PhD experience( 3 %n ethnography requires the researcher to embed themselves in a community and for me this would not have been possible without the openness and generosity of the sta, in both organisations( I am grateful to 6homas Schult:;2agow who agreed to let me carry out the research at %mnesty and I am thankful to Dorota )anat who took time out of her work to help me get settled at %mnesty and carry out my research. I am grateful for the openness of Stephanie Hankey who let me carry out the research despite not knowing much about me beforehand. I am grateful to my mum, &airi &acintyre( 6he quality of my work, and my ability to take criticism, would not be possible without her ‘red pen’ proof reads( She is an inspiring conversational partner, relentless cheerleader, and an incredible support through the highs and lows of life decisions( I am also grateful to %lison =akoura who gave me enthusiasm for +nding the right words to express myself. 4inally, I am grateful to every single person ; friends, colleagues, and sometimes strangers ; who have listened to me repeatedly try to formulate my research question, theory and +ndings ; thank you for your intrigue which made me feel this research has a place in the world. 4 Abstract 6his research examines common claims about how personal data is used in political communication, focusing on civil society organisations (CS0s?( 6wo ethnographic case studies are carried out to investigate the di,erences between a traditional membership-run CS0, %mnesty International, and a grant;funded CS0, 6actical 6echnology Collective( 6he +ndings are threefold. 4irstly, new civil society organisations, such as %vaa:, *@ Degrees and Change.org, assert that data;driven technologies support their audience;led models( However, both organisations in this research engage in data;driven practices to persuade the audience to support the strategy set by organisational sta,, corroborating the critical claims that data;driven practices reinforce expert;led models( Secondly, rhetoric around the uptake of new data;driven practices has been based on the assumption that distinct data-driven ways of working have become normalised. 6he +ndings show, however, that these two CS0s still rely on deliebration, personal -udgement, and relationships to make strategic decisions( 4inally, decision-making surrounding data;driven practices can be influenced by the opaque role of data scientists and data technologies( 6he +ndings show how placing these agents outside of strategic decision-making a,ects the organisation’s ability to manage personal data consistently across projects( 6he research is signi+cant in understanding the complexity and nuance in the adoption, and re-ection, of new data;driven practices( 4urther, the research makes a case for practitioners and researchers alike to be cautious about claims that data;driven practices support audience;led models, and to be open to the bene+ts of expert;led models( 5 Contents List of Tables % List of Figures '( Chapter 1. Data-Driven Campaigns: Success Stories an# Scan#als '' 1.1 More Than A ToolB Data Logic " 1.2 Acceptable and Unacceptable Engagement with Data Logic 1.3 The Research Problem, Aim and Questions * 1.4 Thesis Plan DD Chapter 2. Data Logic, Political Communication an# Agency ,- 2.1 Data LogicB An Ideal Type of Data Practices D@ 2.2 Political Communication and DataB The Trustee and Delegate &odels E! 2.2.1 Data Justice in Political Communication E" 2.2.2 The Trustee and Delegate Models and Responsive Leadership E# 2.3 A Framework for Examining Data Justice in CS0s @F 2.3.1 Delegates and Data Logic @@ 2.3.2 Trustees and Data Logic #* 2.3.4 Summary: Data Logic and Political Representation #E 2.4 Agency and Decision-Making in Data Logic #@ 2.4.1 Technocrats ## 2.4.2 Software "! 2.4.3 The Data Double "!D 2.5 Conclusion """ 6 Chapter 3. Methodology: Two Ethnographic Case Stu#ies ''* 3.1 The Research Questions "" 3.2 The Advantages of an Ethnographic Approach ""D 3.3 Limitations, Ris's and Mitigation Strategies ""# 3.4 Case StudiesB A Comparison of Two Ethnographies " 3.5 Indicators " G 3.6 The Method "*! 3.7 Summary "D" Chapter 4. Data Logic an# Political Representation "D* 4.1 The Internal Balance Between the Trustee and Delegate Models "DG 4.2 Data Logic Absent in the Delegate Model "F" 4.3 Data Logic Absent from the Trustee Model "G! 4.4 Data Logic Supports the Trustees Model "G@ 4.5 Concerns of Using Data Logic to Support Representation "@* 4.6 Summary !D Chapter 5. The Principles of Data Logic *(1 5.1 Contexts in which Data Logic is Apparent !@ 5.1.1 Communications Through Digital Platforms, Events and Traditional Media !# 5.1.2 Fundraising " 5.1.3 Data Logic for Abstract Growth Goals "F 5.1.4 Presenting Information to Others "E 5.1.5 Summary " 5.2 Context in which Data Logic is Re-ected 5.2.1 Long-6erm Goals and Short;6erm Data * 7 5.2.2 Technical Standardised Processes Lead to False Data F 5.2.3 Context;4ree Data # 5.2.4 Conclusion *D 5.3 Summary *F Chapter 6. The Technocrats, Software an# Data Doubles *.- 6.1 TechnocratsB Integrated or Isolated *@ 6.1.1 Isolated Skills of Technocrats *# 6.1.2 Expertise Integrated into Teams and Roles DG 6.1.3 Technocrats and Logics That Do and Do Not Match D# 6.2 Software Choices Managed by Logic F* 6.3 Data doubleB False but Useful FE Chapter 7. Conclusion *1, 7.1 Trustee and Delegate Models and Data logic G# 7.2 The Adoption of Data Logic and Alternatives ED 7.3 Limitations and Further Research @" 7.4 Conclusion and The Contribution of This Research @D Appen#i2 A *!* Appen#i2 B *!F Appen#i2 C *!E 8 List of Tables 6able 1.1: The four principles of data logic(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((( " 6able 2.1: Di,erent terms for centralised and decentralised structures of decision-making in political communication................................................................................................................D@ 6able 4.1: The priority criteria used by Amnesty to guide decisions presented in a conference to other CS0s and academics .........................................................................................................1GE 9 List of Figures 4igure 1.1: Framework for the Use of Personal Data in Political Communication......................40 4igure 1.2 Traditional Decision-Making Roles in the Trustee and Delegate Models((((((((((((((((((D" 4igure "(* Decision-Making Roles which have been Disrupted by Data Logic in the 6rustee and Delegate Models(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((D 4igure ("B %vaa:’s website homepage shows the use of quanti+ed metrics in order to represent themselves (Avaa:, 2019).............................................................................................................63