User:Jydl/Filter bubble


 * 1) Barack Obama's farewell address under Similar Concepts does not really name a specific concept, and the short paragraph is just a direct citation of the speech. Since there is no important correlation to the topic of meaningful interpretation, I would recommend omitting the mention of the farewell address.
 * 2) Platform Studies under Reactions and studies section is much longer than the other two, I would tighten the content to be consistent with the amount of information for each section.

Copied from Filter bubble: A study by researchers from Oxford, Stanford, and Microsoft examined the browsing histories of 1.2 million U.S. users of the Bing Toolbar add-on for Internet Explorer between March and May 2013. They selected 50,000 of those users who were active consumers of news, then classified whether the news outlets they visited were left- or right-leaning, based on whether the majority of voters in the counties associated with user IP addresses voted for Obama or Romney in the 2012 presidential election. They then identified whether news stories were read after accessing the publisher's site directly, via the Google News aggregation service, via web searches, or via social media. The researchers found that while web searches and social media do contribute to ideological segregation, the vast majority of online news consumption consisted of users directly visiting left- or right-leaning mainstream news sites, and consequently being exposed almost exclusively to views from a single side of the political spectrum. Limitations of the study included selection issues such as Internet Explorer users skewing higher in age than the general internet population; Bing Toolbar usage and the voluntary (or unknowing) sharing of browsing history selecting for users who are less concerned about privacy; the assumption that all stories in left-leaning publications are left-leaning, and the same for right-leaning; and the possibility that users who are not active news consumers may get most of their news via social media, and thus experience stronger effects of social or algorithmic bias than those users who essentially self-select their bias through their choice of news publications (assuming they are aware of the publications' biases).

Additional edit: A study by researchers from Princeton University and New York University, aimed to study the impact of filter bubble and algorithmic filtering on social media polarization. They used a mathematical model called the "stochastic block model" to test their hypothesis on the environments of Reddit and Twitter. The researchers gauged changes in polarization in regularized social media networks and non-regularized networks, specifically measuring the percent changes in polarization and disagreement on Reddit and Twitter. They found that polarization increased significantly at 400% in non-regularized networks, while polarization increased by 4% in regularized networks and disagreement by 5%.