How have the design of algorithms used by social media platforms shaped democratic debate?
To what extent should there be greater accountability for the design of these algorithms?
 Jennifer Cobbe and Jatinder Singh, ‘Regulating Recommending: Motivations, Considerations, and Principles’ [https://ssrn.com/abstract=3371830].
 Nick Srnicek (2016) Platform Capitalism, Polity Press.
 Patrick Barwise and Leo Watkins (2018) ‘The evolution of digital dominance: how and why we got to GAFA’, In Martin Moore and Damian Tambini (eds.) Digital Dominance: The Power of Google, Amazon, Facebook, and Apple, Oxford University Press [http://lbsresearch.london.edu/914/]..
 Directive 98/34/EC of the European Parliament and of the Council of 22 June 1998 laying down a procedure for the provision of information in the field of technical standards and regulations (Official Journal L 204 , 21/07/1998 P. 0037 – 0048) (‘Technical Standards and Regulations Directive’), art.1 (as amended by Directive 98/48/EC of the European Parliament and of the Council of 20 July 1998 amending Directive 98/34/EC laying down a procedure for the provision of information in the field of technical standards and regulations (Official Journal L 217 , 05/08/1998 P. 0018 – 0026)); Directive 2000/31/EC of the European Parliament and of the Council of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the Internal Market (Official Journal L 178 , 17/07/2000 P. 0001 – 0016) (‘E-Commerce Directive’), recital 18.
 E-Commerce Directive, article 2(b).
 Shoshana Zuboff (2019) The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, Profile Books.
 Tim Wu (2016) The Attention Merchants: The Epic Scramble to Get Inside Our Heads, Penguin Random House.
 Jesus Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez (2013) 'Recommender systems survey', Knowledge-Based Systems, 46, pp.109-132 [https://www.sciencedirect.com/science/article/abs/pii/S0950705113001044].
 For a legally-accessible discussion of machine learning, see David Lehr and Paul Ohm (2017) ‘Playing with the Data: What Legal Scholars Should Learn About Machine Learning’ 51 U.C. Davis Law Review
 For example, Netflix (Carlos A Gomez-Uribe and Neil Hunt (2015) ‘The Netflix Recommender System: Algorithms, Business Value, and Innovation’, ACM Transactions on Management Information Systems, 6(4) [https://dl.acm.org/citation.cfm?id=2843948].
 For an overview, see Jennifer Cobbe and Jatinder Singh, ‘Regulating Recommending: Motivations, Considerations, and Principles’ [https://ssrn.com/abstract=3371830], pp.9-11.
 Nick Seaver (2018) ‘Captivating algorithms: recommender systems as traps’, Journal of Material Culture.
 YouTube Creator Blog (2012) YouTube Now: Why We Focus on Watch Time [https://youtube-creators.googleblog.com/2012/08/youtube-now-why-we-focus-on-watch-time.html].
 Gomez-Uribe and Hunt (2015) p.7.
 Qingyun Wu, Hongning Wang, Liangjie Hong, and Yue Shi (2017) ‘Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems’, CIKM '17 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management [https://dl.acm.org/citation.cfm?id=3133025].
 Carlos A Gomez-Uribe and Neil Hunt (2015) ‘The Netflix Recommender System: Algorithms, Business Value, and Innovation’, ACM Transactions on Management Information Systems, 6(4) [https://dl.acm.org/citation.cfm?id=2843948], p.5.
 Ashley Rodriguez (2018) ‘YouTube's recommendations drive 70% of what we watch’, Quartz [https://qz.com/1178125/youtubes-recommendations-drive-70-of-what-we-watch].
 Ulrich Dolata (2017) ‘Apple, Amazon, Google, Facebook, Microsoft: Market concentration - competition - innovation strategies’, Stuttgarter Beiträge zur Organisations- und Innovationsforschung, SOI Discussion Paper, No. 2017-01 [https://ideas.repec.org/p/zbw/stusoi/201701.html].
 For example, Zoë Beery (2019) 'How YouTube reactionaries are breaking the news media', Columbia Journalism Review [https://www.cjr.org/analysis/youtube-breaking-news.php]; Jessie Daniels (2018) 'The Algorithmic Rise of the "Alt-Right"', Contexts, 17(1) [https://journals.sagepub.com/doi/10.1177/1536504218766547]; Samantha Bradshaw and Phillip N Howard (2018) ‘Why Does Junk News Spread So Quickly Across Social Media? Algorithms, Advertising and Exposure in Public Life’, Oxford Internet Institute / Knight Foundation [https://comprop.oii.ox.ac.uk/research/working-papers/why-does-junk-news-spread-so-quickly-across-social-media/]; Jonas Kaiser (2018) 'How YouTube helps to unite the Right', Alexander von Humboldt Institute for Internet and Society - Digital Society Blog [https://www.hiig.de/en/how-youtube-helps-to-unite-the-right]; Adrienne Massanari (2017) ‘#Gamergate and The Fappening: How Reddit is algorithm, governance, and culture support toxic technocultures’, new media & society, 19(3), pp.329-346 [https://journals.sagepub.com/doi/abs/10.1177/1461444815608807]; Derek O'Callaghan, Derek Greene, Maura Conway, Joe Carthy, Pádraig Cunningham (2014) ‘Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems’, Social Science Computer Review, 33(4), pp.459-478 [https://journals.sagepub.com/doi/abs/10.1177/0894439314555329?journalCode=ssce].
 For example, Renee DiResta (2019) 'How Amazon's Algorithms Curated a Dystopian Bookstore', Wired [https://www.wired.com/story/amazon-and-the-spread-of-health-misinformation]; Paul Lewis (2018) ''Fiction is outperforming reality': how YouTube's algorithm distorts truth', The Guardian [https://www.theguardian.com/technology/2018/feb/02/how-youtubes-algorithm-distorts-truth]; Caroline O'Donovan, Charlie Warzel, Logan McDonald, Brian Clifton, and Max Woolf (2019) 'We Followed YouTube's Recommendation Algorithm Down The Rabbit Hole', Buzzfeed News [https://www.buzzfeednews.com/article/carolineodonovan/down-youtubes-recommendation-rabbithole]; John C Paolillo (2018) ‘The Flat Earth Phenomenon on YouTube’, First Monday, 23(12) [https://firstmonday.org/ojs/index.php/fm/article/view/8251/7693]; Matt Reynolds (2019) 'Think Facebook has an anti-vaxxer problem? You should see Amazon', Wired [https://www.wired.co.uk/article/facebook-anti-vaccine-disinformation]; Kelly Weill (2018) 'How YouTube Built a Radicalization Machine for the Far-Right', The Daily Beast [https://www.thedailybeast.com/how-youtube-pulled-these-men-down-a-vortex-of-far-right-hate]; Julia Carrie Wong (2019) 'How Facebook and YouTube help spread anti-vaxxer propaganda', The Guardian [https://www.theguardian.com/media/2019/feb/01/facebook-youtube-anti-vaccination-misinformation-social-media].
 For a review of the literature, see Jennifer Cobbe and Jatinder Singh, ‘Regulating Recommending: Motivations, Considerations, and Principles’ [https://ssrn.com/abstract=3371830], pp.20-28.
 Brandy Zadrozny (2019) 'Drowned out by the algorithm: Vaccination advocates struggle to be heard online', NBC News [https://www.nbcnews.com/tech/tech-news/drowned-out-algorithm-pro-vaccination-advocates-struggle-be-heard-online-n976321].
 Josephine B Schmitt, Diana Rieger, Olivia Rutkowski, and Julian Ernst (2018), ‘Counter-messages as Prevention or Promotion of Extremism?! The Potential Role of YouTube’, Journal of Communications, 68 [https://academic.oup.com/joc/article/68/4/780/5042003].
 Jianshu Weng, Ee Peng Lim, Jing Jiang, and Qi He (2010) 'Twitterrank: Finding topic-sensitive influential Twitterers', Proceedings of the Third ACM International Conference on Web Search & Data Mining: February 3-6, 2010, New York, pp. 261-270 [https://dl.acm.org/citation.cfm?id=1718520]; M D Conover, J Ratkiewicz, M Francisco, B Goncalves, A Flammini, and F Menczer (2011) 'Political Polarization on Twitter', Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media [https://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewFile/2847/3275]; Antoine Boutet, Hyoungshick Kim, and Eiko Yoneki (2012) 'What’s in Twitter: I know what parties are popular and who you are supporting now!’, Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining, pp.132–129 [https://ieeexplore.ieee.org/document/6425772]; Robert Faris, Hal Roberts, Bruce Etling, Nikki Bourassa, Ethan Zuckerman, and Yochai Benkler (2017) 'Partizanship, Propaganda, and Disinformation: Online Media and the 2016 U.S. Presidential Election', Berkman Klein Center for Internet & Society Research Publication No. 2017-6, p.71 [https://cyber.harvard.edu/publications/2017/08/mediacloud]; Elanor Colleoni, Alessandro Rozza, and Adam Arvidsson (2014) 'Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data', Journal of Communication, 64, pp.317-332 [https://onlinelibrary.wiley.com/doi/10.1111/jcom.12084]; Eytan Bakshy, Solomon Messing, and Lada A Adamic (2015) ‘Exposure to ideologically diverse news and opinion on Facebook’, Science, 348(6239) [https://science.sciencemag.org/content/348/6239/1130]; Seth Flaxman, Shared Goel, Justin M Rao (2016) ‘Filter Bubbles, Echo Chambers, and Online News Consumption’, Public Opinion Quarterly, 80(S1) [https://academic.oup.com/poq/article/80/S1/298/2223402].
 Richard Fletcher and Rasmus Kleis Nielsen (2017) ‘Are News Audiences Increasingly Fragmented? A Cross-National Comparative Analysis of Cross-Platform News Audience Fragmentation and Duplication’, Journal of Communication, 67(4) [https://onlinelibrary.wiley.com/doi/abs/10.1111/jcom.12315]; see also Frederik Zuiderveen Borgesius, Damian Trilling, Judith Moeller, Balázs Bodó, Clas H. de Vreese, and Natalie Helberger (2015) ‘Should We Worry About Filter Bubbles?’, Internet Policy Review, 5(1a), pp.3-5 [https://policyreview.info/articles/analysis/should-we-worry-about-filter-bubbles]; Judith Möller, Damian Trilling, Natali Helberger, and Bram van Es (2018) ‘Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity’, Information, Communication & Society, 21(7), pp.959-977 [https://www.tandfonline.com/doi/full/10.1080/1369118X.2018.1444076]; Mario Haim, Andreas Graefe, and Hans-Bernd Brosius (2018) ‘Burst of the Filter Bubble?’, Digital Journalism, 6(3), pp.330-343 [https://www.tandfonline.com/doi/abs/10.1080/21670811.2017.1338145].
 Mark Leiser (2016) ‘AstroTurfing, 'CyberTurfing' and other online persuasion campaigns’, European Journal of Law and Technology, 7(1) [http://ejlt.org/article/view/501].
 Mark Leiser (2016) ‘AstroTurfing, 'CyberTurfing' and other online persuasion campaigns’, European Journal of Law and Technology, 7(1) [http://ejlt.org/article/view/501]; for a review of the literature, see Rose Marie Santini, Larissa Agostini, Carlos Eduardo Barros, Danilo Carvalho, Rafael Centeno de Rezende, Debora G Salles, Kenzo Seto, Camyla Terra, and Giulia Tuccy (2018) ‘Software Power as Soft Power: A literature review on computational propaganda and political process’, PArtecipazione e COnflitto, 11(2) [http://siba-ese.unisalento.it/index.php/paco/article/view/19546]; see also Samantha Bradshaw and Phillip N Howard (2017) ‘Troops, Trolls and Troublemakers: A Global Inventory of Organized Social Media Manipulation’, Computational Propaganda Research Project Working paper no 2017.12, Oxford Internet Institute [https://comprop.oii.ox.ac.uk/research/troops-trolls-and-trouble-makers-a-global-inventory-of-organized-social-media-manipulation/]; Bence Kollyani, Philip N Howard, and Samuel C Woolley (2016) ‘Bots and Automation over Twitter during the U.S. Election’, Data Memo 2016.4. Oxford, UK: Project on Computational Propaganda [https://comprop.oii.ox.ac.uk/research/working-papers/bots-and-automation-over-twitter-during-the-u-s-election/]; Emilio Ferrara (2017) ‘Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election’, First Monday [https://firstmonday.org/ojs/index.php/fm/article/view/8005/6516]; Alessandro Bessi and Emilio Ferrara (2016) ‘Social bots distort the 2016 U.S. Presidential election online discussion’, First Monday, 21(11) [https://firstmonday.org/article/view/7090/5653]; Muhammad Nihal Hussein, Serpil Tokdemir, Nitin Agarwal, and Samer Al-Khateeb (2018) ‘Analyzing Disinformation and Crowd Manipulation Tactics on YouTube’, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) [https://ieeexplore.ieee.org/document/8508766].
 Directive 2000/31/EC of the European Parliament and of the Council of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the Internal Market (Official Journal L 178, 17/07/2000 P. 0001 – 0016) (‘E-Commerce Directive’).
 See, e.g., Jennifer Cobbe and Jatinder Singh, ‘Regulating Recommending: Motivations, Considerations, and Principles’ [https://ssrn.com/abstract=3371830], pp.28-31.
 Regulation (EU) 2019/1150 of the European Parliament and of the Council of 20 June 2019 on promoting fairness and transparency for business users of online intermediation services (Official Journal L 186, 11/7/2019, P. 57–79).
 Zeynep Tufekci (2016), ‘As the Pirates Become CEOs: The Closing of the Open Internet’, Dædalus, the Journal of the American Academy of Arts & Sciences, 145(1), p.74 [https://www.mitpressjournals.org/doi/abs/10.1162/DAED_a_00366?journalCode=daed]; Natascha Just and Michael Latzer (2017) ‘Governance by algorithms: reality construction by algorithmic selection on the Internet’, Media, Culture & Society, 39(2), pp.238-258 [https://journals.sagepub.com/doi/abs/10.1177/0163443716643157?journalCode=mcsa]; James G Webster (2010) ‘User Information Regimes: How Social Media Shape Patterns of Consumption’, Northwestern University Law Review, 104(2); Zeynep Tufekci (2015) ‘Algorithmic Harms Beyond Facebook and Google: Emergent Challenges of Computational Agency’, Colorado Technology Law Journal, 13, pp.207-208. [https://ctlj.colorado.edu/wp-content/uploads/2015/08/Tufekci-final.pdf]; Taina Bucher (2012) 'Want to be on top? Algorithmic power and the threat of invisibility on Facebook', New Media & Society, 14(7), pp.1164-1180 [https://journals.sagepub.com/doi/abs/10.1177/1461444812440159]; Taina Bucher (2017) 'The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms', Information, Communication & Society, 20(1), pp.30-44 [https://www.tandfonline.com/doi/abs/10.1080/1369118X.2016.1154086].
 For elaboration on these principles, see Jennifer Cobbe and Jatinder Singh, ‘Regulating Recommending: Motivations, Considerations, and Principles’ [https://ssrn.com/abstract=3371830], pp.34-43.