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      Users Polarization on Facebook and Youtube

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          Abstract

          Users online tend to select information that support and adhere their beliefs, and to form polarized groups sharing the same view—e.g. echo chambers. Algorithms for content promotion may favour this phenomenon, by accounting for users preferences and thus limiting the exposure to unsolicited contents. To shade light on this question, we perform a comparative study on how same contents (videos) are consumed on different online social media—i.e. Facebook and YouTube—over a sample of 12 M of users. Our findings show that content drives the emergence of echo chambers on both platforms. Moreover, we show that the users’ commenting patterns are accurate predictors for the formation of echo-chambers.

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          Science vs Conspiracy: collective narratives in the age of (mis)information

          The large availability of user provided contents on online social media facilitates people aggregation around common interests, worldviews and narratives. However, in spite of the enthusiastic rhetoric about the so called {\em wisdom of crowds}, unsubstantiated rumors -- as alternative explanation to main stream versions of complex phenomena -- find on the Web a natural medium for their dissemination. In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives -- i.e. main stream scientific and alternative news -- are consumed on Facebook. Through a thorough quantitative analysis, we show that distinct communities with similar information consumption patterns emerge around distinctive narratives. Moreover, consumers of alternative news (mainly conspiracy theories) result to be more focused on their contents, while scientific news consumers are more prone to comment on alternative news. We conclude our analysis testing the response of this social system to 4709 troll information -- i.e. parodistic imitation of alternative and conspiracy theories. We find that, despite the false and satirical vein of news, usual consumers of conspiracy news are the most prone to interact with them.
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            Statistical inference for stochastic simulation models--theory and application.

            Statistical models are the traditional choice to test scientific theories when observations, processes or boundary conditions are subject to stochasticity. Many important systems in ecology and biology, however, are difficult to capture with statistical models. Stochastic simulation models offer an alternative, but they were hitherto associated with a major disadvantage: their likelihood functions can usually not be calculated explicitly, and thus it is difficult to couple them to well-established statistical theory such as maximum likelihood and Bayesian statistics. A number of new methods, among them Approximate Bayesian Computing and Pattern-Oriented Modelling, bypass this limitation. These methods share three main principles: aggregation of simulated and observed data via summary statistics, likelihood approximation based on the summary statistics, and efficient sampling. We discuss principles as well as advantages and caveats of these methods, and demonstrate their potential for integrating stochastic simulation models into a unified framework for statistical modelling. © 2011 Blackwell Publishing Ltd/CNRS.
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              Opinion dynamics on interacting networks: media competition and social influence

              The inner dynamics of the multiple actors of the informations systems – i.e, T.V., newspapers, blogs, social network platforms, – play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2016
                23 August 2016
                : 11
                : 8
                : e0159641
                Affiliations
                [1 ]IUSS, Pavia, Italy
                [2 ]CSSLab, IMT Lucca, Italy
                [3 ]ISC, CNR, Rome, Italy
                [4 ]NICO, Northwestern University, Evanston, IL, United States of America
                University of Warwick, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceived and designed the experiments: AB AS WQ.

                • Performed the experiments: AB MP.

                • Analyzed the data: AB MP AS GC BU WQ.

                • Contributed reagents/materials/analysis tools: AB MP FZ MD.

                • Wrote the paper: AB FZ MD MP AS GC BU WQ.

                Article
                PONE-D-16-17171
                10.1371/journal.pone.0159641
                4994967
                27551783
                a116f8cb-bace-4573-a6a2-2c4c1d13d821
                © 2016 Bessi et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 April 2016
                : 6 July 2016
                Page count
                Figures: 6, Tables: 6, Pages: 24
                Funding
                Funding for this work was provided by EU FET project MULTIPLEX nr. 317532, SIMPOL nr. 610704, DOLFINS nr. 640772, SOBIGDATA nr. 654024. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Facebook
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                Network Analysis
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                Custom metadata
                The entire data collection process has been carried out exclusively through the Facebook Graph API and the YouTube Data API. The collection methods are relayed in the Data Collection section.

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