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      No echo in the chambers of political interactions on Reddit

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          Abstract

          Echo chambers in online social networks, whereby users’ beliefs are reinforced by interactions with like-minded peers and insulation from others’ points of view, have been decried as a cause of political polarization. Here, we investigate their role in the debate around the 2016 US elections on Reddit, a fundamental platform for the success of Donald Trump. We identify Trump vs Clinton supporters and reconstruct their political interaction network. We observe a preference for cross-cutting political interactions between the two communities rather than within-group interactions, thus contradicting the echo chamber narrative. Furthermore, these interactions are asymmetrical: Clinton supporters are particularly eager to answer comments by Trump supporters. Beside asymmetric heterophily, users show assortative behavior for activity, and disassortative, asymmetric behavior for popularity. Our findings are tested against a null model of random interactions, by using two different approaches: a network rewiring which preserves the activity of nodes, and a logit regression which takes into account possible confounding factors. Finally, we explore possible socio-demographic implications. Users show a tendency for geographical homophily and a small positive correlation between cross-interactions and voter abstention. Our findings shed light on public opinion formation on social media, calling for a better understanding of the social dynamics at play in this context.

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          A critical point for random graphs with a given degree sequence

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            When Corrections Fail: The Persistence of Political Misperceptions

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              Political science. Exposure to ideologically diverse news and opinion on Facebook.

              Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook's algorithmically ranked News Feed and further studied users' choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals' choices played a stronger role in limiting exposure to cross-cutting content.
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                Author and article information

                Contributors
                gdfm@isi.it
                corrado.monti@isi.it
                michele.starnini@isi.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                2 February 2021
                2 February 2021
                2021
                : 11
                : 2818
                Affiliations
                GRID grid.418750.f, ISNI 0000 0004 1759 3658, ISI Foundation, ; Via Chisola 5, 10126 Turin, Italy
                Article
                81531
                10.1038/s41598-021-81531-x
                7854755
                33531520
                998269e4-c777-47e8-aaaf-0648f39c4023
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 July 2020
                : 5 January 2021
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                complex networks,computer science,human behaviour
                Uncategorized
                complex networks, computer science, human behaviour

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