Bursting the Filter Bubble
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BURSTINGTHE FILTER BUBBLE:DEMOCRACY , DESIGN, AND ETHICS Proefschrift ter verkrijging van de graad van doctor aan de Technische Universiteit Delft, op gezag van de Rector Magnificus prof. ir. K. C. A. M. Luyben, voorzitter van het College voor Promoties, in het openbaar te verdedigen op woensdag, 16 September 2015 om 10:00 uur door Engin BOZDAG˘ Master of Science in Technische Informatica geboren te Malatya, Turkije. Dit proefschrift is goedgekeurd door: Promotors: Prof. dr. M.J. van den Hoven Prof. dr. ir. I.R. van de Poel Copromotor: dr. M.E. Warnier Samenstelling promotiecommissie: Rector Magnificus, voorzitter Prof. dr. M.J. van den Hoven Technische Universiteit Delft, promotor Prof. dr. ir. I.R. van de Poel Technische Universiteit Delft, promotor dr. M.E. Warnier Technische Universiteit Delft, copromotor Independent members: dr. C. Sandvig Michigan State University, USA Prof. dr. M. Binark Hacettepe University, Turkey Prof. dr. R. Rogers Universiteit van Amsterdam Prof. dr. A. Hanjalic Technische Universiteit Delft Prof. dr. ir. M.F.W.H.A. Janssen Technische Universiteit Delft, reservelid Printed by: CPI Koninklijke Wöhrmann Cover Design: Özgür Taylan Gültekin E-mail: [email protected] WWW: http://www.bozdag.nl Copyright © 2015 by Engin Bozda˘g All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, includ- ing photocopying, recording or by any information storage and retrieval system, without written permission of the author. An electronic version of this dissertation is available at http://repository.tudelft.nl/. PREFACE For Philip Serracino Inglott, For his passion and dedication to Information Ethics Rest in Peace. Engin Bozda˘g Delft, August 24, 2015 iii CONTENTS 1 Introduction1 1.1 Background.................................2 1.2 Problem Statement.............................8 1.3 Research Methodology...........................9 1.4 Contributions................................ 11 2 Bias in Algorithmic Filtering and Personalization 13 2.1 Introduction................................ 14 2.2 Information overload and the rise of the filters............... 15 2.3 Personalization – a technical overview................... 17 2.4 A model of Filtering for Online Web Services................ 20 2.4.1 Source Selection Algorithm..................... 21 2.4.2 Information Selection and Prioritization Algorithm......... 22 2.4.3 Human operator........................... 23 2.4.4 Personalization Algorithm...................... 25 2.5 Discussion................................. 29 2.5.1 Implications for an ethical analysis................. 29 2.5.2 Implications for design........................ 32 2.5.3 Implications for the design of social filtering............. 32 2.5.4 Implications for social network analysis............... 34 2.6 Conclusion................................. 35 3 Viewpoint Diversity in Online Social Networks - An Empirical Analysis 37 3.1 Introduction................................ 37 3.2 Empirical Studies of Information Diversity in Social Media......... 39 3.3 Theory................................... 40 3.3.1 Information Diversity........................ 40 3.3.2 Dimension of Information Diversity................. 41 3.3.3 Minorities and Openness...................... 42 3.4 Polarization in the Netherlands and Turkey................. 42 3.4.1 the Netherlands........................... 43 3.4.2 Turkey................................ 43 3.4.3 Conclusion.............................. 45 3.5 Method................................... 45 3.5.1 Data collection............................ 45 3.5.2 Research questions......................... 45 3.5.3 Entropy............................... 46 3.5.4 Translating Research Questions into Metrics............. 46 v vi CONTENTS 3.6 Results................................... 48 3.6.1 Distribution of seed users and their followers............ 48 3.6.2 Seed User Bias............................ 49 3.6.3 Source and Output Diversity..................... 49 3.6.4 Minority Access........................... 50 3.6.5 Input Output Correlation...................... 50 3.7 Limitations................................. 52 3.8 Discussion................................. 52 4 Democracy, Filter Bubble and Design 55 4.1 Introduction................................ 55 4.2 Democracy: different theories, different benchmarks............ 56 4.2.1 Liberal View of Democracy...................... 57 4.2.2 Deliberative Democracy....................... 58 4.2.3 Republicanism and Contestatory Democracy............ 59 4.2.4 Agonism / Inclusive Political Communication............ 61 4.2.5 Conclusion.............................. 62 4.3 Software Design to Combat Filter Bubbles................. 65 4.3.1 Liberal / User Autonomy Enhancing................. 65 4.3.2 Deliberative / Enhancing Epistemic Quality of Information..... 68 4.4 Discussion................................. 71 4.5 Conclusion................................. 77 5 Ethics of Online Social Experiments 79 5.1 The Facebook Emotional Contagion Experiment.............. 79 5.2 The Unclear Ethical Terrain of Online Social Experiments.......... 80 5.3 Arguments For and Against Online Social Experiments........... 81 5.3.1 Benefits of Online Experiments for the Individual.......... 82 5.3.2 Informed Consent and Its Many Interpretations........... 83 5.3.3 The Ubiquity of Online Social Experiments............. 84 5.3.4 Different Perceptions of Risk in Online Experiments......... 85 5.3.5 Benefits of Online Experimentation for the Society......... 85 5.3.6 The Unavoidability of Online Experiments.............. 86 5.4 Discussion................................. 87 6 Conclusion 91 6.1 Answers to the Research Questions and Revisiting the Objectives of the Study.................................... 92 6.2 Limitations of the Study........................... 94 6.3 Future Research............................... 95 6.3.1 Viewpoint Diversity Research.................... 95 6.3.2 Transparency............................ 97 Acknowledgements 101 Summary 103 Samenvatting 107 CONTENTS vii Curriculum Vitæ 111 6.4 Scientific Publications........................... 111 6.4.1 Journal Articles............................ 111 6.4.2 Conference Proceedings....................... 111 6.5 Professional/Popular Publications..................... 112 References 113 1 INTRODUCTION The peculiar evil of silencing the expression of an opinion is, that it is robbing the human race; posterity as well as the existing generation; those who dissent from the opinion, still more than those who hold it. If the opinion is right, they are deprived of the opportunity of exchanging error for truth: if wrong, they lose, what is almost as great a benefit, the clearer perception and livelier impression of truth, produced by its collision with error. John Stuart Mill, philosopher, 1859 Should we go to war? How high should the taxes be? What happened at Gezi Park in Istanbul? These are all political questions where different sources would give a differ- ent answer. With the advent of social media as an important source of news and opin- ions [183, 230], some activists and scholars have started worrying that the Internet could lead to online segregation and may increase radicalism and extremism in society, due to receiving biased and one-sided news and opinions. Critics have pointed out the dan- gers of group forming among like-minded in Internet. Recently, online platforms such as Facebook and Google have been criticized, because with their opaque personaliza- tion algorithms they show users viewpoints that they already agree with, hence lead- ing to information silos, or so-called filter bubbles. The reason why filter bubbles have been criticized differs. Some argue that the opaque algorithms used by online platforms make decisions on behalf of the user, coercing him and making him unaware of available choices. Others argue that biases caused by algorithms and human beings themselves might diminish viewpoint diversity, decrease respect toward one another or allow op- pressors to prevail due to a lack of information to the citizens, which will prevent them reaching the truth or only one side of the truth. Viewpoint diversity has long been viewed as an essential component of strong democratic societies [82, 150]. Yet others, including Google and Facebook, have argued that the effects of personalization have been exagger- ated [26, 249, 352]. The immediate question that comes to mind is whether filter bub- bles really exist. However, to answer this question empirically and properly, one must first define what filter bubble is. Do online platforms really have biases that may cause bubbles? Do people themselves construct self-reinforcing filters because they already 1 2 1.I NTRODUCTION have divergent beliefs? This interdisciplinary thesis aims to answer such questions by 1 studying filter bubble phenomenon and the relevant value viewpoint diversity 1) at the conceptual level by using theories from by political philosophy, media and communica- tion and ethics 2) at the empirical level by analyzing filter bubble by extending viewpoint diversity analytics metrics 3) at the technical level, by analyzing tools that are designed to combat filter bubbles. 1.1. BACKGROUND According to a study in the US, nearly half (48%) of the panelists say they accessed news about politics and government on Facebook alone in the past week [230]. More than twice as many Americans are following political candidates on social media in