Quantifying echo chamber effects in information spreading over political communication networks

Abstract

Echo chambers in online social networks, in which users prefer to interact only with ideologically-aligned peers, are believed to facilitate misinformation spreading and contribute to radicalize political discourse. In this paper, we gauge the effects of echo chambers in information spreading phenomena over political communication networks. Mining 12 million Twitter messages, we reconstruct a network in which users interchange opinions related to the impeachment of the former Brazilian President Dilma Rousseff. We define a continuous political position parameter, independent of the network's structure, that allows to quantify the presence of echo chambers in the strongly connected component of the network, reflected in two well-separated communities of similar sizes with opposite views of the impeachment process. By means of simple spreading models, we show that the capability of users in propagating the content they produce, measured by the associated spreadability, strongly depends on their attitude. Users expressing pro-impeachment sentiments are capable to transmit information, on average, to a larger audience than users expressing anti-impeachment sentiments. Furthermore, the users' spreadability is correlated to the diversity, in terms of political position, of the audience reached. Our method can be exploited to identify the presence of echo chambers and their effects across different contexts and shed light upon the mechanisms allowing to break echo chambers.

The echo chamber effect on social media

“…Note that polarization can happen independently from the structure or the very presence of social interactions. Homophily in social interactions can be quantified by representing interactions as a social network and then analyzing its structure concerning the opinions of the users ( 18 , 39 , 40 ). Social networks can be reconstructed in different ways from online social media, where links represent social relationships or interactions.…”

“…The network’s topology can reveal echo chambers, where users are surrounded by peers with similar leanings, and thus they get exposed, with a higher probability, to similar contents. In network terms, this translates into a node with a given leaning more likely to be connected with nodes with a leaning close to ( 18 ). This concept can be quantified by defining, for each user , the average leaning of their neighborhood, as , where is the adjacency matrix of the interaction network, if there is a link from node to node , otherwise, and is the out-degree of node .…”

“…Simple models of information spreading can gauge the presence of echo chambers: Users are expected to be more likely to exchange information with peers sharing a similar leaning ( 18 , 42 , 43 ). Classical epidemic models such as the susceptible–infected–recovered (SIR) model ( 44 ) have been used to study the diffusion of information, such as rumors or news ( 45 – 47 ).…”

“…Our attention span remains limited ( 9 , 10 ), and feed algorithms might limit our selection process by suggesting contents similar to the ones we are usually exposed to ( 11 – 13 ). Furthermore, users show a tendency to favor information adhering to their beliefs and join groups formed around a shared narrative, that is, echo chambers ( 1 , 14 – 18 ). We can broadly define echo chambers as environments in which the opinion, political leaning, or belief of users about a topic gets reinforced due to repeated interactions with peers or sources having similar tendencies and attitudes.…”

How behavioural sciences can promote truth, autonomy and democratic discourse online

“…These asymmetries in Big Tech's business model have created an opaque information ecology that undermines not only user autonomy but also the transparent exchange on which democratic societies are built 8,9 . Several problematic social phenomena pervade the internet, such as the spread of false information [10][11][12][13][14] -which includes disinformation (intentionally fabricated falsehoods) and misinformation (falsehoods created without intent, for example, poorly researched content or biased reporting)-or attitudinal and emotional polarization 15,16 (for example, polarization of elites 17 , partisan sorting 18 and polarization with respect to controversial topics 19,20 ). Some disinformation and misinformation involve public health and safety; some of it undermines processes of self-governance.…”