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. 2021 Mar 2;118(9):e2023301118.
doi: 10.1073/pnas.2023301118.

The echo chamber effect on social media

Matteo Cinelli  1 , Gianmarco De Francisci Morales  2 , Alessandro Galeazzi  3 , Walter Quattrociocchi  4 , Michele Starnini  2
Affiliations

The echo chamber effect on social media

Matteo Cinelli et al. Proc Natl Acad Sci U S A. .

Abstract

Social media may limit the exposure to diverse perspectives and favor the formation of groups of like-minded users framing and reinforcing a shared narrative, that is, echo chambers. However, the interaction paradigms among users and feed algorithms greatly vary across social media platforms. This paper explores the key differences between the main social media platforms and how they are likely to influence information spreading and echo chambers' formation. We perform a comparative analysis of more than 100 million pieces of content concerning several controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. We quantify echo chambers over social media by two main ingredients: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter. We conclude the paper by directly comparing news consumption on Facebook and Reddit, finding higher segregation on Facebook.

Keywords: echo chambers; information spreading; polarization; social media.

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
Joint distribution of the leaning of users x and the average leaning of their neighborhood xNN for different datasets. (A) Twitter, (B) Reddit, (C) Facebook, and (D) Gab. Colors represent the density of users: The lighter the color, the larger the number of users. Marginal distribution P(x) and PN(x) are plotted on the x and y axes, respectively. Facebook and Twitter present by homophilic clustering.
Fig. 2.
Fig. 2.
Size and average leaning of communities detected in different datasets. A and C show the full spectrum of leanings related to the topics of abortions and vaccines with regard to communities in B and D, where the political leaning is less sparse.
Fig. 3.
Fig. 3.
Average leaning ⟨μ(x)⟩ of the influence sets reached by users with leaning x, for different datasets under consideration. Size and color of each point represent the average size of the influence sets. The parameters of the SIR dynamics are set to (A) β=0.10⟨k⟩−1, (B) β=0.01⟨k⟩−1, (C) β=0.05⟨k⟩−1, and (D) β=0.05⟨k⟩−1, while ν is fixed at 0.2 for all simulations.
Fig. 4.
Fig. 4.
Direct comparison of news consumption on (A) Facebook and (B) Reddit. Joint distribution of the leaning of users x and the average leaning of their nearest neighbor xN (Top), size and average leaning of communities detected in the interaction networks (Middle), and average leaning ⟨μ(x)⟩ of the influence sets reached by users with leaning x, by running SIR dynamics (Bottom) with parameters β=0.05⟨k⟩ for A, β=0.006⟨k⟩ for B, and ν=0.2 for both. Facebook presents a highly segregated structure with regard to Reddit.
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