Modeling Echo Chambers and Polarization Dynamics in Social Networks

Echo chambers and opinion polarization recently quantified in several sociopolitical contexts and across different social media raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena remain unclear. We propose a model that introduces the dynamics of radicalization as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Inspired by empirical findings on social interaction dynamics, we consider agents characterized by heterogeneous activities and homophily. We show that the transition between a global consensus and emerging radicalized states is mostly governed by social influence and by the controversialness of the topic discussed. Compared with empirical data of polarized debates on Twitter, the model qualitatively reproduces the observed relation between users’ engagement and opinions, as well as opinion segregation in the interaction network. Our findings shed light on the mechanisms that may lie at the core of the emergence of echo chambers and polarization in social media.

Figure 1

Temporal evolution of the agents’ opinions. (a) Neutral consensus for which all opinions converge to zero ($\alpha =0.05$, $\beta =2$). (b) (One-sided) radicalization ($\alpha =3$, $\beta =0$). (c) Opinion polarization, in which opinions split into two opposite sides ($\alpha =3$, $\beta =3$). Social interaction strength and reciprocity were set to $K=3$ and $r=0.5$, respectively. Positive [negative] opinions $\sigma (x_i)>0$ [$\sigma (x_i)<0$] are colored in blue [red]. Note different scales on the $y$ axis.

Figure 2

Transition from consensus to radicalization dynamics. Absolute values of the average final opinions $|⟨x_f⟩|$ in the $K\text{−}\alpha$ phase space for $\beta =0.5$ and $r=0.5$. In the dark region, the system approaches a neutral consensus, while in the brighter areas the population undergoes radicalization dynamics which become more pronounced for increasing values of $K$ and/or $\alpha$ (color code).

Figure 3

Activity vs opinion. (a) Average activity $⟨a⟩$ of users as a function of their political leaning $x$, for three empirical datasets. (b) Activity-opinion density plot of ${10}^3$ polarized opinion states for $K=2$, $\alpha =3$, $\beta =1$, and $r=0.65$. The colors encode the value of $\rho (a,x)$ which is normalized with respect to $N$.

Figure 4

Echo chambers. Contour maps for the average opinion of the nearest neighbors $⟨x{⟩}^{\mathrm{NN}}$ against a user’s opinion $x$, for 200 simulations of the radicalization model with $K=2.5$, $\alpha =4.5$, $\beta =2$, and $r=0.65$ (a) and three different datasets (b)–(d). Colors represent the density of users: the lighter the color, the larger the number of users. The marginal distribution of opinions $P(x)$ and average opinions of the nearest neighbor $P_{\mathrm{NN}}(x)$ are plotted on the $x$ and $y$ axis, respectively.