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. 2016 Aug 23;11(8):e0159641.
doi: 10.1371/journal.pone.0159641. eCollection 2016.

Users Polarization on Facebook and Youtube

Alessandro Bessi  1   2 , Fabiana Zollo  2 , Michela Del Vicario  2 , Michelangelo Puliga  2 , Antonio Scala  2   3 , Guido Caldarelli  2   3 , Brian Uzzi  4 , Walter Quattrociocchi  2   3
Affiliations

Users Polarization on Facebook and Youtube

Alessandro Bessi et al. PLoS One. .

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 12M 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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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Correlation Matrix.
Spearman’s rank correlation coefficients between users’ actions on Facebook posts and the related YouTube videos.
Fig 2
Fig 2. Consumption Patterns of Videos on Facebook and YouTube.
The empirical CCDFs, 1 − F(x), show the consumption patterns of videos supporting conflicting narratives—i.e. Science and Conspiracy—in terms of comments (A and C) and likes (B and D) on Facebook and YouTube.
Fig 3
Fig 3. Polarization on Facebook and YouTube.
The PDFs of the polarization ρ show that the vast majority of users is polarized towards one of the two conflicting narratives—i.e. Science and Conspiracy—on both Facebook and YouTube.
Fig 4
Fig 4. Commenting Activity of Polarized Users.
The empirical CCDFs, 1 − F(x), of the number of comments left by polarized users on Facebook and YouTube.
Fig 5
Fig 5. Commenting Activity of Users Polarized towards Conflicting Narratives.
The empirical CCDFs, 1 − F(x), of the number of comments left by users polarized on scientific narratives and conspiracy theories on Facebook (A) and YouTube (B).
Fig 6
Fig 6. Performance measures the classification task.
Precision, recall, and accuracy of the classification task for users Polarized in Conspiracy, Not Polarized, Polarized in Science on Facebook and YouTube as a function of n. On both online social networks, we find that the model’s performance measures monotonically increase as a function of n. Focusing on the accuracy, significant results (greater than 0.70) are obtained for low values of n.
See this image and copyright information in PMC

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