reading

Abul-Fottouh, Deena et al. Examining algorithmic biases in YouTube’s recommendations of vaccine videos. International journal of medical informatics (Shannon, Ireland), 2020, Vol. 8/140 — Library resource

Alfano, Mark et al. Technologically scaffolded atypical cognition: the case of YouTube’s recommender system. Synthese (Dordrecht), 2020

Bayerischer Rundfunk. Blackbox reporting. December 2023 & auf Deutsch

Bergen, Mark. YouTube executives ignored warnings, letting toxic videos run rampant, 2019

Brindle, James. Something is wrong on the internet. Medium. 2017

Chaslot, Guillaume. The toxic potential of YouTube’s feedback loop. 2020. Video. 27’

Chaslot, Guillaume. How algorithms can learn to discredit the media. Medium. 2018

Chaslot, Guillaume. How YouTube’s AI boosts alternative facts. 2017

Covington, Paul, Adams, Jay & Sargin, Emre. Deep neural networks for YouTube recommendations. Google. 2016

Diakopoulos, Nick. Investigating data platforms and algorithms. Data Journalism Handbook 2. 2021

Diakopoulos, Nicholas & Trielli, Daniel. How journalists can systematically critique algorithms. Computation & Journalism Symposium 2020

Diakopoulos, Nick. Algorithmic Accountability: On the investigation of black boxes. CJR. 2014

Diresta, Renee. Up Next: a better recommender system. Wired. 2018

Dolan, Peter et al. Personalized news recommendation based on click behavior. Google. 2009?

Duhigg, Charles. How companies learn your secrets. New York Times. 2012

Eriksson, Maria et al. How does Spotify package music? Spotify teardown: inside the black box of streaming music. The MIT Press. 2019, p. 115-47.

Facebook. What are Facebook recommendations? 2020 & What are Instagram recommendations? 2020

Goldschmitt, KE & Seaver, Nick. Shaping the stream: techniques and troubles of algorithmic recommendation. In Cook, Nicholas et al. The Cambridge Companion to Music in Digital Culture. 2019, p. 63-81

GOV.UK. Research into the impact of streaming services’ algorithms on music consumption. Centre for data ethics and innovation, (Dept. for science, innovation and technology), Feb 2023

Hallinan, Blake & Striphas, Ted. Recommended for you: The Netflix Prize and the production of algorithmic culture. New media and society. 2016

Hardesty, Larry. Explained: neural networks. MIT, 2017

Helberger, Natali. “On the democratic role of news recommenders”. Digital Journalism: algorithms, automation & news, 7/8 (2019), p. 993-1012. Also in Thurman, Neil (ed.). Algorithms, Automation & News. Routledge, 2021

Holmes, Dawn E., Recommender systems. Big Data: a very short introduction. OUP VSI. 2017, p. 80-89 — Library resource

Jones, Elliot. Inform, educate, entertain…and recommend? Exploring the use and ethics of recommendation systems in public service media [BBC]. Ada Lovelace Institute, November 2022.

Kuosmanen, Ville. Improving the explainability of user-facing recommender systems. Thesis, St Andrews. 2020

Leibowicz, Claire & Stephan, Adriana. Local Newsrooms Should Adopt AI Ethics as They Adopt AI: 5 Recommendations

Lewis, Paul & McCormick, Erin. How an ex YouTube insider investigated its secret algorithm. Guardian. 2018

Maheshwari. Sapna. On YouTube Kids, startling videos slip past filters. NY Times, 2017

Marconi, Francesco, Daldrup, Rajiv & Tilland, Pant. Acing the algorithm beat. 2019. Nieman Lab. 2019

Milano, Silvia, Taddeo, Mariarosaria & Floridi, Luciano. Recommender systems and their ethical challenges. Oxford Internet Institute. 2019

Nadeem. Moin. How YouTube recommends videos. Medium. 2018

O’Callaghan, Derek et al. Down the (White) Rabbit Hole: The extreme right and online recommender systems. Social science computer review. 2015, 33/4, p. 459-78

O’Neill, Cathy. Weapons of math destruction. Penguin, 2020 — Library resource (online & hard copy)

Pariser, Eli. The filter bubble: what the internet is hiding from you. Penguin, 2011 — Library resource

Ricci Francesco et al. Recommender Systems: Introduction and Challenges. In Ricci Francesco et al. (eds.) Recommender systems handbook. Springer, 2015 — Library resource

Rieder, Bernhard. Introducing the YouTube data tools. 2015 & YouTube data tools (1.22)

Rogers, Richard. YouTube teardown. Doing Digital Methods. 2019, p. 249-59

Sanchez Bocanegra et al. HealthRecSys: A semantic content-based recommender system to complement health videos. BMC Medical Informatics and Decision Making. p. 17-63. 2017

Seaver, Nick. Captivating algorithms: Recommender systems as traps. Journal of Material Culture. 24/4, 2019. p. 421–36

Seaver, Nick. Computing taste. University of Chicago Press, 2022

Smith, Ben. How Tiktok reads your mind. NY Times, 2021

Stray, Jonathan, Adler, Steven & Hadfield-Menell, Dylan. What are you optimizing for? Aligning recommender systems with human values. 2020

Thomson, Clive. YouTube’s plot to silence conspiracy theories. Wired. 2020

Thurman, Neil et al. (eds.). Algorithms, Automation & News. Routledge, 2021. Library resource See also Digital Journalism, vol 7/8, 2019. Library resource