@inproceedings{SpinelliCrovella:FairUMAP20,
  author	= {Spinelli, Larissa and Crovella, Mark},
  title		= {How {YouTube} Leads {Privacy-Seeking} Users Away from Reliable Information},
  booktitle	= {Proceedings of the Workshop on Fairness in User Modeling, Adaptation, and Personalization (FairUMAP)},
  year		= {2020},
  month		= jul,
  address	= {Online},
  abstract	= {Online media is increasingly selected and filtered by recommendation engines. YouTube is one of the most significant sources of socially-generated information, and as such its recommendation policies are important to understand. Because of YouTube's revenue model, the nature of its recommendation policies is fairly opaque. Hence, we present an empirical exploration of the nature of YouTube recommendations, concentrating on socially-impactful dimensions. First, we confirm that YouTube's recommendations generally ``lead away'' from reliable information sources, with a tendency to direct users over time toward video channels exposing extreme and unscientific viewpoints. Second, we show that there is a fundamental tension between user privacy and extreme recommendations. We show that in general, users who seek privacy by keeping personal information hidden, receive much more extreme and unreliable recommendations from the YouTube engine. This drawback of user privacy in the presence of recommender systems has not been widely appreciated. We quantify this effect along various dimensions, including its dynamics in time, and show that the tradeoff between privacy and unreliability of recommendations is generally pervasive in the YouTube recommendation process.},
  doi		= {10.1145/3386392.3399566},
  URL		= {http://www.cs.bu.edu/faculty/crovella/paper-archive/youtube-fairumap20.pdf}
}