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On Quantifying Knowledge Segregation in Society


Chakraborty,  Abhijnan
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;


Gummadi,  Krishna
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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Chakraborty, A., Ali, M., Ghosh, S., Ganguly, N., & Gummadi, K. (2017). On Quantifying Knowledge Segregation in Society. Retrieved from http://arxiv.org/abs/1708.00670.

Cite as: https://hdl.handle.net/21.11116/0000-0000-DF0F-6
With rapid increase in online information consumption, especially via social media sites, there have been concerns on whether people are getting selective exposure to a biased subset of the information space, where a user is receiving more of what she already knows, and thereby potentially getting trapped in echo chambers or filter bubbles. Even though such concerns are being debated for some time, it is not clear how to quantify such echo chamber effect. In this position paper, we introduce Information Segregation (or Informational Segregation) measures, which follow the long lines of work on residential segregation. We believe that information segregation nicely captures the notion of exposure to different information by different population in a society, and would help in quantifying the extent of social media sites offering selective (or diverse) information to their users.