Big Data and Public Service Media. A Literature Review of Key Challenges and some Theoretical Propositions Pertaining to the Context of Digital Transformation A Review of the Literature and Some First Propositions on its Effects on PSM Values
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Veröffentlicht
Jan. 13, 2021
Paul Clemens Murschetz
Abstract
Der vorliegende Beitrag befasst sich mit den Herausforderungen von Big Data auf das Wertesystem öffentlich-rechtlicher Massenmedien. Es wird argumentiert, dass die Beforschung von Prozessen der „Datafizierung“ als neuem Paradigma auch für öffentliche Medien eine stärkere Berücksichtigung kritischer sozialwissenschaftlicher Fragen erfordert, um unser Verständnis davon zu fördern, dass Big Data auch prosoziale und nicht alleine technokratische und ökonomische Werte fördert und erzielt, die mit dem Wertesystem von öffentlichen Medien in Einklang stehen. Es prüft wissenschaftliche Literatur zu den wichtigsten Herausforderungen von Datafizierung auf öffentlich-rechtliche Medien und entwickelt drei wichtige Annahmen, die vermuten lassen, dass der öffentlich-rechtlichen Auftrag im digitalen Zeitalter gefährdet ist.
Zitationsvorschlag
Murschetz, Paul Clemens. 2021. „Big Data and Public Service Media. A Literature Review of Key Challenges and Some Theoretical Propositions Pertaining to the Context of Digital Transformation: A Review of the Literature and Some First Propositions on Its Effects on PSM Values“. MedienJournal 44 (3):69-86. https://doi.org/10.24989/medienjournal.v44i3.1808.
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References
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Amatriain, X., & Basilico, J. (2015). Recommender systems in industry: A Netflix case study. In F. Ricci, L. Rokach, & B. Shapira (Eds.). Recommender Systems Handbook (pp. 385–419). Boston: Springer.
Athique, A. (2018). The dynamics and potentials of big data for audience research. Media, Culture and Society, 40(1), 59–74.
Birkbak, A., & Carlsen, H. B. (2016). The public and its algorithms: Comparing and experimenting with calculated publics. In L. Amoore, & V. Piotukh (Eds.). Algorithmic Life: Calculative Devices in the Age of Big Data (pp. 21–34). London: Routledge.
Baumann, S., & Hasenpusch, T. C. (2016). Multi-Platform Television and Business Models: A Babylonian Clutter of Definitions and Concepts. Westminster Papers in Communication and Culture, 11(1), 85–102. http://dx.doi.org/10.16997/wpcc.219
Carah, N. (2017). Algorithmic brands: A decade of brand experiments with mobile and social media. New Media & Society, 19(3), 384–400.
Bughin, J. (2016). Big data, Big bang? Journal of Big Data, 3(2). doi: 10.1186/s40537-015-0014-3
Burri, M. (2015). Contemplating a ‘public service navigator’: In search of new (and better) functioning public service media. International Journal of Communication, 9, 1341–1359.
Burri, M., & Helberger, N. (2015). Public Service Media and Exposure Diversity: Introduction. International Journal of Communication, 9, 1319–1323.
Chan-Olmsted, S. (2019). Media Adoption of Cognitive Technologies: The Strategic Applications and Implications of Artificial Intelligence in the Media Industry. Paper presentation to 2019 Annual Conference of the European Media Management Association (emma). Media Management and Actionable Knowledge: The Relationship between Theory and Practice. Limassol, 5–7 June.
Curry, E. (2016). The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches. In J. M. Cavanillas, E. Curry, & W. Wahlster (Eds.). Horizons for a Data-Driven Economy (pp. 245–259). Heidelberg: Springer.
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
Doyle, G. (2018). Television and the development of the data economy: data analysis, power and the public interest. International Journal of Digital Television, 9(1), 53–68.
Downes, L., & Nunes, P. (2014). Big Bang Disruption: Strategy in the Age of Devastating Innovation. https://hbr.org/2013/03/big-bang-disruption.
EBU (2016a). Big Data Initiative Workshop – Algorithms and Society. www.ebu.ch/contents/events/2016/12/big-data-initiative-workshop-algorithms-and-society.html]
EBU (2016b). PSM in the 21st Century: What value and which values? Geneva: European Broadcasting Union.
EBU (2017a). The Future of Audience Measurement. Geneva: European Broadcasting Union.
EBU (2017b). Big data initiative. Time to Invest. Geneva: European Broadcasting Union.
EBU (2018a). Big Data Conference. Geneva, 28th of February to 1st of March, 2018. https://www.ebu.ch/events/2018/02/big-data-conference-2018.
EBU (2018b). Big Data Initiative. Activity Report. https://www.ebu.ch/events/2018/02/big-data-conference-2018. Geneva, 28th of February to 1st of March, 2018.
EBU (2018c). Learing from Public Service Media in Digital Transformation. Geneva: European Broadcasting Union.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data Consumer Analytics and the Transformation of Marketing. Journal of Business Research, 6(2), 897–904.
Evans, J. (2017, April 20). Big Data won’t drive creativity, big insights will. Brand Quarterly. http://www.brandquarterly.com/big-data-wont-drive-creativity-big-insights-will
Evens, T., & Van Damme, K. (2016). Consumers’ willingness to share personal data: Implications for newspapers’ business models. International Journal on Media Management, 18(1), 25–41.
Ben Fields, R. J., & Cowlishaw, T. (2018). The Case for Public Service Recommender Algorithms, 22-24. https://piret.gitlab.io/fatrec2018/program/fatrec2018-fields.pdf
Foer, F. (2017). World without Mind: The Existential Threat of Big Tech. New York: Penguin Press.
Gandhi, B., & Martinez-Smith, A., & Kuhlman, D. (2015). TV insights: Applications of big data to television. https://www.arris.com/globalassets/resources/white-papers/arris_applyingbigdatatotv_whitepaper_final.pdf
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35, 137–144.
Glowacki, M., & Jackson, L. (2014). Public media management for the twenty first century. Creativity, innovation and interaction. London, New York: Routledge.
Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions of Management Information Systems, 6(4), 1–13:19.
Gonser, N., & Gundlach, H. (2015). Public Value. In J. Krone, & T. Pellegrini (Hrsg.). Handbuch Medienökonomie. Springer Reference Sozialwissenschaften. Wiesbaden: Springer VS. https://doi.org/10.1007/978-3-658-09632-8_48-1
Hjarvard, S. (2018). Public Service in the Age of Social Network Media. In G. F. Lowe, H. Van den Bulck, & K. Donders (Eds.). Public Service Media in the Networked Society (pp. 59–74). Göteborg: Nordicom.
Holtzhausen, D. (2016). Datafication: threat or opportunity for communication in the public sphere? Journal of Communication Management, 20(1), 21–36.
Jackson, L. (2017). The potential of machine mediation in the maintenance of public service media. In M. Glowacki, & A. Jaskiernia (Eds.). Public Service Media Renewal (pp. 135–155). Frankfurt/Main: Peter Lang.
Kelly, J. P. (2017). Television by the numbers. The challenges of audience measurement in the age of Big Data. Convergence. The International Journal of Research into New Media Technologies. 1–20. https://doi.org/10.1177/1354856517700854
Kim, S. J. (2018). Audience Measurement and Analysis. In A. B. Albarran, B. I. Mierzejewska, & J. Jung (Eds.). The Handbook of Media Management and Economics, 2nd ed. (pp. 379–393). Abingdon, Oxford: Routledge.
Kneale, D. (2016, Jan 21). Big Data Dream. Big data is everywhere-now what to do with it? New tools unlock the secrets of consumer desire. http://www.broadcastingcable.com/news/rights-insights/big-data-dream/147166
Kosterich, A., & Napoli, P. M. (2015). Reconfiguring the audience commodity: The institutionalization of social TV analytics as market information regime. Television & New Media, 17(3), 254–271. doi:10.1177/1527476415597480
Kunz, R., Siebert, J., & Mütterlein, J. (2015). The Media Balanced Scorecard. Diskussionspapier Nr. 04-15. University of Bayreuth. http://www.fiwi.uni-bayreuth.de/de/download/WP_04-15.pdf
Lippell, H. (2016). Big Data in the Media and Entertainment Sectors. In J. M. Cavanillas, E. Curry, & Wahlster, W. (Eds.). New Horizons for a Data-Driven Economy. A Roadmap for Usage and Exploitation of Big Data in Europe. doi 10.1007/978-3-319-21569-3_1
Livingstone, S. (2018). Audiences in an age of datafication: critical questions for media research. Television & New Media. https://doi.org/10.1177/1527476418811118
Lowe, G. F., Van den Bulck, H., & Donders, K. (2018). Public Service Media in the Networked Society. RIPE@2017. Göteborg: Nordicom.
Madrigal, A. C. (2014). How Netflix reverse-engineered Hollywood. The Atlantic. http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/
Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.). Debates in the Digital Humanities (pp. 460–475). Minneapolis: University of Minnesota Press.
Meynhardt, T. (2009). Public Value Inside: What is Public Value Creation? International Journal of Public Administration, 32(3–4), 192–219.
Meynhardt, T., Gomez, P. & Schweizer, M. (2014). The Public Value Scorecard: what makes an organization valuable to society? https://www.alexandria.unisg.ch/229529/1/EY-Performance-Organization-valuable-to-society1.pdf
Moore, M. (1995). Creating Public Value – Strategic Management in Government. Cambridge: Harvard University Press.
Murschetz, P. C. (2016). Connected television: Media convergence, industry structure and corporate strategies. In E. L. Cohen (Ed.). Communication Yearbook 40 (pp. 69–93). New York, NY: Routledge. http://dx.doi.org/10.1080/23808985.2015.11735256.
Murschetz, P. C. (2017). Big Data and the Economics of TV Broadcasting. A Challenging Agenda. Medien Journal – Zeitschrift für Medien- und Kommunikationsforschung, 3/2017. https://doi.org/10.24989/medienjournal.v41i3.1492.
Murschetz, P. C., & Schlütz, D. (2018). Big Data and Television Broadcasting. A Critical Reflection on Big Data’s Surge to Become a New Techno-Economic Paradigm and its Impacts on the Concept of the “Addressable Audience”. Fonseca: Journal of Communication. University of Salamanca, 17. https://doi.org/10.14201/fjc2018172338.
Napoli, P. M. (2016). The audience as product, consumer, and producer in the contemporary media marketplace. In G. F. Lowe, & Brown, C. (Eds.). Managing Media Firms and Industries: What's So Special About Media Management? (pp. 261-275). Berlin: Springer International Publishing.
Nelson, J. L., & Webster, J. G. (2016). Audience currencies in the age of big data. International Journal of Media Management, 18(1), 9–24.
Perez, C. (2010). Technological revolutions and techno-economic paradigms. Cambridge Journal of Economics, 34(1), 185–202.
Schäfer, M. T., & Van Es, K. (2017). The Datafied Society. Studying Culture through Data. Amsterdam: Amsterdam University Press.
Schmidt, J.-H., & Sørensen, J. K., Dreyer, S., & Hasebrink, U. (2018). Wie können Empfehlungssysteme zur Vielfalt von Medieninhalten beitragen? Perspektiven für öffentlich-rechtliche Rundfunkanstalten. Media Perspektiven, 11, 522–531.
Schroeder, R. (2016). Big data business models: Challenges and opportunities. Cogent Social Sciences, 2, 1–25.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286.
Schwarz, J. A. (2015). Public Service Broadcasting and Data-Driven Personalization: A View from Sweden. Television & New Media. https://doi.org/10.1177/1527476415616193
Sørensen, J. K. & Hutchinson, J. (2018). Algorithms and Public Service Media. In G. F. Lowe, Gregory Ferrell, H. Van den Bulck, & K. Donders (Eds.). Public Service Media in the Networked Society (pp. 91–106). Göteborg: Nordicom.
Sørensen, J. K. (2019). Public Service Media, Diversity and Algorithmic Recommendation: Tensions between Editorial Principles and Algorithms in European PSM Organizations. RecSys 2019: 13th ACM Conference on Recommender Systems – 7th International Workshop on News Recommendation and Analytics. Copenhagen, Denmark.
Stone, M. L. (2014): Big data for media. Oxford, UK: Reuters Institute for the Study of Journalism.
Suárez Candel, R. (2017). Preface: Facing Criticims and a new reality – the need for a new service public media mindset. In M. Glowacki, & A. Jaskiernia (Eds.). Public Service Media Renewal. Adaption to Digital Network Challenges (pp. 7–13). Frankfurt/Main: Peter Lang.
Tole, A. A. (2013). Big data challenges. Database Systems Journal, 4(3), 31–40.
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
Van den Bulck, H., & Moe, H. (2018). Public service media, universality and personalisation through algorithms: mapping strategies and exploring dilemmas. Media, Culture & Society, 40(6), 875–892. https://doi.org/10.1177/0163443717734407
Van Es, K. (2017). An Impending Crisis of Imagination: Data-Driven Personalization in Public Service Broadcasters. Media@LSE Working Paper Series. http://www.lse.ac.uk/media@lse/research/mediaWorkingPapers/pdf/Working-Paper-43.pdf
Amatriain, X., & Basilico, J. (2015). Recommender systems in industry: A Netflix case study. In F. Ricci, L. Rokach, & B. Shapira (Eds.). Recommender Systems Handbook (pp. 385–419). Boston: Springer.
Athique, A. (2018). The dynamics and potentials of big data for audience research. Media, Culture and Society, 40(1), 59–74.
Birkbak, A., & Carlsen, H. B. (2016). The public and its algorithms: Comparing and experimenting with calculated publics. In L. Amoore, & V. Piotukh (Eds.). Algorithmic Life: Calculative Devices in the Age of Big Data (pp. 21–34). London: Routledge.
Baumann, S., & Hasenpusch, T. C. (2016). Multi-Platform Television and Business Models: A Babylonian Clutter of Definitions and Concepts. Westminster Papers in Communication and Culture, 11(1), 85–102. http://dx.doi.org/10.16997/wpcc.219
Carah, N. (2017). Algorithmic brands: A decade of brand experiments with mobile and social media. New Media & Society, 19(3), 384–400.
Bughin, J. (2016). Big data, Big bang? Journal of Big Data, 3(2). doi: 10.1186/s40537-015-0014-3
Burri, M. (2015). Contemplating a ‘public service navigator’: In search of new (and better) functioning public service media. International Journal of Communication, 9, 1341–1359.
Burri, M., & Helberger, N. (2015). Public Service Media and Exposure Diversity: Introduction. International Journal of Communication, 9, 1319–1323.
Chan-Olmsted, S. (2019). Media Adoption of Cognitive Technologies: The Strategic Applications and Implications of Artificial Intelligence in the Media Industry. Paper presentation to 2019 Annual Conference of the European Media Management Association (emma). Media Management and Actionable Knowledge: The Relationship between Theory and Practice. Limassol, 5–7 June.
Curry, E. (2016). The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches. In J. M. Cavanillas, E. Curry, & W. Wahlster (Eds.). Horizons for a Data-Driven Economy (pp. 245–259). Heidelberg: Springer.
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
Doyle, G. (2018). Television and the development of the data economy: data analysis, power and the public interest. International Journal of Digital Television, 9(1), 53–68.
Downes, L., & Nunes, P. (2014). Big Bang Disruption: Strategy in the Age of Devastating Innovation. https://hbr.org/2013/03/big-bang-disruption.
EBU (2016a). Big Data Initiative Workshop – Algorithms and Society. www.ebu.ch/contents/events/2016/12/big-data-initiative-workshop-algorithms-and-society.html]
EBU (2016b). PSM in the 21st Century: What value and which values? Geneva: European Broadcasting Union.
EBU (2017a). The Future of Audience Measurement. Geneva: European Broadcasting Union.
EBU (2017b). Big data initiative. Time to Invest. Geneva: European Broadcasting Union.
EBU (2018a). Big Data Conference. Geneva, 28th of February to 1st of March, 2018. https://www.ebu.ch/events/2018/02/big-data-conference-2018.
EBU (2018b). Big Data Initiative. Activity Report. https://www.ebu.ch/events/2018/02/big-data-conference-2018. Geneva, 28th of February to 1st of March, 2018.
EBU (2018c). Learing from Public Service Media in Digital Transformation. Geneva: European Broadcasting Union.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data Consumer Analytics and the Transformation of Marketing. Journal of Business Research, 6(2), 897–904.
Evans, J. (2017, April 20). Big Data won’t drive creativity, big insights will. Brand Quarterly. http://www.brandquarterly.com/big-data-wont-drive-creativity-big-insights-will
Evens, T., & Van Damme, K. (2016). Consumers’ willingness to share personal data: Implications for newspapers’ business models. International Journal on Media Management, 18(1), 25–41.
Ben Fields, R. J., & Cowlishaw, T. (2018). The Case for Public Service Recommender Algorithms, 22-24. https://piret.gitlab.io/fatrec2018/program/fatrec2018-fields.pdf
Foer, F. (2017). World without Mind: The Existential Threat of Big Tech. New York: Penguin Press.
Gandhi, B., & Martinez-Smith, A., & Kuhlman, D. (2015). TV insights: Applications of big data to television. https://www.arris.com/globalassets/resources/white-papers/arris_applyingbigdatatotv_whitepaper_final.pdf
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35, 137–144.
Glowacki, M., & Jackson, L. (2014). Public media management for the twenty first century. Creativity, innovation and interaction. London, New York: Routledge.
Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions of Management Information Systems, 6(4), 1–13:19.
Gonser, N., & Gundlach, H. (2015). Public Value. In J. Krone, & T. Pellegrini (Hrsg.). Handbuch Medienökonomie. Springer Reference Sozialwissenschaften. Wiesbaden: Springer VS. https://doi.org/10.1007/978-3-658-09632-8_48-1
Hjarvard, S. (2018). Public Service in the Age of Social Network Media. In G. F. Lowe, H. Van den Bulck, & K. Donders (Eds.). Public Service Media in the Networked Society (pp. 59–74). Göteborg: Nordicom.
Holtzhausen, D. (2016). Datafication: threat or opportunity for communication in the public sphere? Journal of Communication Management, 20(1), 21–36.
Jackson, L. (2017). The potential of machine mediation in the maintenance of public service media. In M. Glowacki, & A. Jaskiernia (Eds.). Public Service Media Renewal (pp. 135–155). Frankfurt/Main: Peter Lang.
Kelly, J. P. (2017). Television by the numbers. The challenges of audience measurement in the age of Big Data. Convergence. The International Journal of Research into New Media Technologies. 1–20. https://doi.org/10.1177/1354856517700854
Kim, S. J. (2018). Audience Measurement and Analysis. In A. B. Albarran, B. I. Mierzejewska, & J. Jung (Eds.). The Handbook of Media Management and Economics, 2nd ed. (pp. 379–393). Abingdon, Oxford: Routledge.
Kneale, D. (2016, Jan 21). Big Data Dream. Big data is everywhere-now what to do with it? New tools unlock the secrets of consumer desire. http://www.broadcastingcable.com/news/rights-insights/big-data-dream/147166
Kosterich, A., & Napoli, P. M. (2015). Reconfiguring the audience commodity: The institutionalization of social TV analytics as market information regime. Television & New Media, 17(3), 254–271. doi:10.1177/1527476415597480
Kunz, R., Siebert, J., & Mütterlein, J. (2015). The Media Balanced Scorecard. Diskussionspapier Nr. 04-15. University of Bayreuth. http://www.fiwi.uni-bayreuth.de/de/download/WP_04-15.pdf
Lippell, H. (2016). Big Data in the Media and Entertainment Sectors. In J. M. Cavanillas, E. Curry, & Wahlster, W. (Eds.). New Horizons for a Data-Driven Economy. A Roadmap for Usage and Exploitation of Big Data in Europe. doi 10.1007/978-3-319-21569-3_1
Livingstone, S. (2018). Audiences in an age of datafication: critical questions for media research. Television & New Media. https://doi.org/10.1177/1527476418811118
Lowe, G. F., Van den Bulck, H., & Donders, K. (2018). Public Service Media in the Networked Society. RIPE@2017. Göteborg: Nordicom.
Madrigal, A. C. (2014). How Netflix reverse-engineered Hollywood. The Atlantic. http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/
Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.). Debates in the Digital Humanities (pp. 460–475). Minneapolis: University of Minnesota Press.
Meynhardt, T. (2009). Public Value Inside: What is Public Value Creation? International Journal of Public Administration, 32(3–4), 192–219.
Meynhardt, T., Gomez, P. & Schweizer, M. (2014). The Public Value Scorecard: what makes an organization valuable to society? https://www.alexandria.unisg.ch/229529/1/EY-Performance-Organization-valuable-to-society1.pdf
Moore, M. (1995). Creating Public Value – Strategic Management in Government. Cambridge: Harvard University Press.
Murschetz, P. C. (2016). Connected television: Media convergence, industry structure and corporate strategies. In E. L. Cohen (Ed.). Communication Yearbook 40 (pp. 69–93). New York, NY: Routledge. http://dx.doi.org/10.1080/23808985.2015.11735256.
Murschetz, P. C. (2017). Big Data and the Economics of TV Broadcasting. A Challenging Agenda. Medien Journal – Zeitschrift für Medien- und Kommunikationsforschung, 3/2017. https://doi.org/10.24989/medienjournal.v41i3.1492.
Murschetz, P. C., & Schlütz, D. (2018). Big Data and Television Broadcasting. A Critical Reflection on Big Data’s Surge to Become a New Techno-Economic Paradigm and its Impacts on the Concept of the “Addressable Audience”. Fonseca: Journal of Communication. University of Salamanca, 17. https://doi.org/10.14201/fjc2018172338.
Napoli, P. M. (2016). The audience as product, consumer, and producer in the contemporary media marketplace. In G. F. Lowe, & Brown, C. (Eds.). Managing Media Firms and Industries: What's So Special About Media Management? (pp. 261-275). Berlin: Springer International Publishing.
Nelson, J. L., & Webster, J. G. (2016). Audience currencies in the age of big data. International Journal of Media Management, 18(1), 9–24.
Perez, C. (2010). Technological revolutions and techno-economic paradigms. Cambridge Journal of Economics, 34(1), 185–202.
Schäfer, M. T., & Van Es, K. (2017). The Datafied Society. Studying Culture through Data. Amsterdam: Amsterdam University Press.
Schmidt, J.-H., & Sørensen, J. K., Dreyer, S., & Hasebrink, U. (2018). Wie können Empfehlungssysteme zur Vielfalt von Medieninhalten beitragen? Perspektiven für öffentlich-rechtliche Rundfunkanstalten. Media Perspektiven, 11, 522–531.
Schroeder, R. (2016). Big data business models: Challenges and opportunities. Cogent Social Sciences, 2, 1–25.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286.
Schwarz, J. A. (2015). Public Service Broadcasting and Data-Driven Personalization: A View from Sweden. Television & New Media. https://doi.org/10.1177/1527476415616193
Sørensen, J. K. & Hutchinson, J. (2018). Algorithms and Public Service Media. In G. F. Lowe, Gregory Ferrell, H. Van den Bulck, & K. Donders (Eds.). Public Service Media in the Networked Society (pp. 91–106). Göteborg: Nordicom.
Sørensen, J. K. (2019). Public Service Media, Diversity and Algorithmic Recommendation: Tensions between Editorial Principles and Algorithms in European PSM Organizations. RecSys 2019: 13th ACM Conference on Recommender Systems – 7th International Workshop on News Recommendation and Analytics. Copenhagen, Denmark.
Stone, M. L. (2014): Big data for media. Oxford, UK: Reuters Institute for the Study of Journalism.
Suárez Candel, R. (2017). Preface: Facing Criticims and a new reality – the need for a new service public media mindset. In M. Glowacki, & A. Jaskiernia (Eds.). Public Service Media Renewal. Adaption to Digital Network Challenges (pp. 7–13). Frankfurt/Main: Peter Lang.
Tole, A. A. (2013). Big data challenges. Database Systems Journal, 4(3), 31–40.
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
Van den Bulck, H., & Moe, H. (2018). Public service media, universality and personalisation through algorithms: mapping strategies and exploring dilemmas. Media, Culture & Society, 40(6), 875–892. https://doi.org/10.1177/0163443717734407
Van Es, K. (2017). An Impending Crisis of Imagination: Data-Driven Personalization in Public Service Broadcasters. Media@LSE Working Paper Series. http://www.lse.ac.uk/media@lse/research/mediaWorkingPapers/pdf/Working-Paper-43.pdf
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