{"title":"Measuring online social bubbles","date":"2015-12-02","doi":"10.7717\/peerj-cs.38","language":"en","pdf_url":"https:\/\/peerj.com\/articles\/cs-38.pdf","fulltext_html_url":"https:\/\/peerj.com\/articles\/cs-38","volume":"1","firstpage":"e38","author":["Dimitar Nikolov","Diego F.M. Oliveira","Alessandro Flammini","Filippo Menczer"],"author_institution":["Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States","Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States","Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States","Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States"],"author_email":"dnikolov@indiana.edu","authors":"Nikolov, Dimitar; Oliveira, Diego F.M.; Flammini, Alessandro; Menczer, Filippo","author_institutions":"Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, United States","keywords":["Bias","Diversity","Polarization","Filter bubble","Echo chamber","Web traffic"],"journal_title":"PeerJ Computer Science","journal_abbrev":"PeerJ Comput. Sci.","publisher":"PeerJ Inc.","issn":"2376-5992","description":"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\u2014Twitter 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 \u201csocial bubbles.\u201d Our results could lead to a deeper understanding of how technology biases our exposure to new information.","description-html":"\u003Cp\u003ESocial 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\u2014Twitter 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 \u201csocial bubbles.\u201d Our results could lead to a deeper understanding of how technology biases our exposure to new information.\u003C\/p\u003E","title-html":"Measuring online social bubbles","subjects":["Network Science and Online Social Networks","Social Computing","World Wide Web and Web Science"],"identifiers":{"peerj":"cs-38","pubmed":null,"pmc":null},"@context":"http:\/\/static.peerj.com\/context\/citation\/context.json","@type":"http:\/\/schema.org\/ScholarlyArticle","@id":"https:\/\/peerj.com\/articles\/cs-38","_links":{"self":{"href":"https:\/\/peerj.com\/articles\/cs-38.json"},"alternate":{"html":{"type":"text\/html","href":"https:\/\/peerj.com\/articles\/cs-38.html"},"xml":{"type":"application\/xml","href":"https:\/\/peerj.com\/articles\/cs-38.xml"},"pdf":{"type":"application\/pdf","href":"https:\/\/peerj.com\/articles\/cs-38.pdf"},"rdf":{"type":"application\/rdf+xml","href":"https:\/\/peerj.com\/articles\/cs-38.rdf"},"ris":{"type":"application\/x-research-info-systems","href":"https:\/\/peerj.com\/articles\/cs-38.ris"},"bib":{"type":"application\/x-bibtex","href":"https:\/\/peerj.com\/articles\/cs-38.bib"},"citeproc":{"type":"application\/vnd.citationstyles.csl+json","href":"https:\/\/peerj.com\/articles\/cs-38.citeproc"},"bibjson":{"type":"application\/bibjson+json","href":"https:\/\/peerj.com\/articles\/cs-38.bibjson"},"unixref":{"type":"application\/unixref+xml","href":"https:\/\/peerj.com\/articles\/cs-38.unixref"}}}}