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Automated audits of recommender systems found that blindly following recommendations leads users to increasingly partisan, conspiratorial, or false content.
A game-theoretic approach to recommendation systems with strategic content providers
Ben-Porat, O.; and Tennenholtz, M. 2018 · 2018
Earlier work this paper cites.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J.; and Gebru, T. 2018 · 2018
Earlier work this paper cites.
Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram
Cotter, K. 2019 · 2019
Earlier work this paper cites.
Auditing radicalization pathways on YouTube
Horta Ribeiro, M.; Ottoni, R.; West, R.; Almeida, V. A.; and Meira Jr, W. 2020 · 2020
Earlier work this paper cites.
Measuring misinformation in video search platforms: An audit study on YouTube
Hussein, E.; Juneja, P.; and Mitra, T. 2020 · 2020
Earlier work this paper cites.
How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance
Shin, D. 2020 · 2020
Earlier work this paper cites.
Examining the consumption of radical content on YouTube
Hosseinmardi, H.; Ghasemian, A.; Clauset, A.; Mobius, M.; Rothschild, D. M.; and Watts, D. J. 2021 · 2021
Earlier work this paper cites.
Preference amplification in recommender systems
Kalimeris, D.; Bhagat, S.; Kalyanaraman, S.; and Weinsberg, U. 2021 · 2021
Cited alongside, same era.
Amplification and its discontents: Why regulating the reach of online content is hard
Keller, D. 2021 · 2021
Cited alongside, same era.
Auditing algorithms: Understanding algorithmic systems from the outside in
Metaxa, D.; Park, J. S.; Robertson, R. E.; Karahalios, K.; Wilson, C.; Hancock, J.; Sandvig, C.; et al. 2021 · 2021
Cited alongside, same era.
Recommender systems and the amplification of extremist content
Whittaker, J.; Looney, S.; Reed, A.; and Votta, F. 2021 · 2021
Cited alongside, same era.
Echo Chambers, Rabbit Holes, and Algorithmic Bias: How YouTube Recommends Content to Real Users
Brown, M. A.; Bisbee, J.; Lai, A.; Bonneau, R.; Nagler, J.; and Tucker, J. A. 2022 · 2022
Cited alongside, same era.
How YouTube Radicalized Brazil
Fischer, M.; and Taub, A. 2019 · 2022
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Haroon, M.; Chhabra, A.; Liu, X.; Mohapatra, P.; Shafiq, Z.; and Wojcieszak, M. 2022 · 2022
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The Algorithmic Crystal: Conceptualizing the Self through Algorithmic Personalization on TikTok
Lee, A. Y.; Mieczkowski, H.; Ellison, N. B.; and Hancock, J. T. 2022 · 2022
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YouTube Regrets
Mozilla Foundation. 2019 · 2022
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Right-wing YouTube: A supply and demand perspective
Munger, K.; and Phillips, J. 2022 · 2022
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“It is just a flu”: Assessing the Effect of Watch History on YouTube’s Pseudoscientific Video Recommendations
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To Recommend or Not? A Model-Based Comparison of Item-Matching Processes
Chang, S.; and Ugander, J. 2022 · 2022
Cited alongside, same era.
Chen, A. Y.; Nyhan, B.; Reifler, J.; Robertson, R. E.; and Wilson, C. 2022 · 2022
Cited alongside, same era.
Papadamou, K.; Zannettou, S.; Blackburn, J.; De Cristofaro, E.; Stringhini, G.; and Sirivianos, M. 2022 · 2022
Later among the works it cites.
What Will ”Amplification” Mean in Court?
Thorburn, L.; Stray, J.; and Bengani, P. 2022 · 2022
Later among the works it cites.