10.7717/peerj-cs.38 20170722033524 PeerJ doi@peerj.com PeerJ PeerJ Computer Science 2376-5992 2015 1 Dimitar Nikolov Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States Diego F.M. Oliveira Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States Alessandro Flammini Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States Filippo Menczer Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. Here we quantitatively measure this kind of social bias at the collective level by mining a massive datasets of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed—Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside “social bubbles.” Our results could lead to a deeper understanding of how technology biases our exposure to new information. 12 02 2015 e38 e38 10.7717/peerj-cs.38 James S. 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