Abstract
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.
Highlights
The rapid adoption of the Web as a source of knowledge and a social space has made it ever more difficult for people to manage the constant stream of news and information arriving on their screens
The observed differences in diversity did not change significantly over a period of three and a half years. This empirical evidence suggests that social media expose the community to a narrower range of information sources, compared to a baseline of information seeking activities
The diversity of targets reached via email seems to be higher than that of social media, the difference is smaller and its statistical significance is weaker due to the larger noise in the data
Summary
The rapid adoption of the Web as a source of knowledge and a social space has made it ever more difficult for people to manage the constant stream of news and information arriving on their screens. Content providers and users have responded to this problem by adopting a wide range of tools and behaviors that filter and/or rank items in the information stream. One important result of this process has been higher personalization (Mobasher, Cooley & Srivastava, 2000)—people see more content tailored to them based on their past behaviors or social networks. Because of the limited time and attention people possess and the large popularity of online social networks, the discovery of information is being transformed
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