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      Measuring online social bubbles

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          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.

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          Most cited references 26

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          The Law of Group Polarization

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            Feeling validated versus being correct: a meta-analysis of selective exposure to information.

            A meta-analysis assessed whether exposure to information is guided by defense or accuracy motives. The studies examined information preferences in relation to attitudes, beliefs, and behaviors in situations that provided choices between congenial information, which supported participants' pre-existing attitudes, beliefs, or behaviors, and uncongenial information, which challenged these tendencies. Analyses indicated a moderate preference for congenial over uncongenial information (d=0.36). As predicted, this congeniality bias was moderated by variables that affect the strength of participants' defense motivation and accuracy motivation. In support of the importance of defense motivation, the congeniality bias was weaker when participants' attitudes, beliefs, or behaviors were supported prior to information selection; when participants' attitudes, beliefs, or behaviors were not relevant to their values or not held with conviction; when the available information was low in quality; when participants' closed-mindedness was low; and when their confidence in the attitude, belief, or behavior was high. In support of the importance of accuracy motivation, an uncongeniality bias emerged when uncongenial information was relevant to accomplishing a current goal. Copyright (c) 2009 APA, all rights reserved.
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              Automatic personalization based on Web usage mining

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                Author and article information

                Contributors
                Journal
                peerj-cs
                PeerJ Computer Science
                PeerJ Comput. Sci.
                PeerJ Inc. (San Francisco, USA )
                2376-5992
                2 December 2015
                : 1
                Affiliations
                Center for Complex Networks and Systems Research, Indiana University , Bloomington, IN, United States
                Article
                cs-38
                10.7717/peerj-cs.38
                © 2015 Nikolov et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                Product
                Self URI (journal-page): https://peerj.com/computer-science/
                Funding
                Funded by: James S. McDonnell Foundation
                Funded by: National Science Foundation
                Award ID: CCF-1101743
                This manuscript is based upon work supported in part by the James S. McDonnell Foundation and the National Science Foundation (award CCF-1101743). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Network Science and Online Social Networks
                Social Computing
                World Wide Web and Web Science

                Computer science

                Echo chamber, Web traffic, Polarization, Filter bubble, Bias, Diversity

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