Comparing information diffusion mechanisms by matching on cascade size

Abstract

Do some types of information spread faster, broader, or further than others? To understand how information diffusions differ, scholars compare structural properties of the paths taken by content as it spreads through a network, studying so-called cascades. Commonly studied cascade properties include the reach, depth, breadth, and speed of propagation. Drawing conclusions from statistical differences in these properties can be challenging, as many properties are dependent. In this work, we demonstrate the essentiality of controlling for cascade sizes when studying structural differences between collections of cascades. We first revisit two datasets from notable recent studies of online diffusion that reported content-specific differences in cascade topology: an exhaustive corpus of Twitter cascades for verified true- or false-news content by Vosoughi et al. [S. Vosoughi, D. Roy, S. Aral. Science 359, 1146–1151 (2018)] and a comparison of Twitter cascades of videos, pictures, news, and petitions by Goel et al. [S. Goel, A. Anderson, J. Hofman, D. J. Watts. Manage. Sci. 62, 180–196 (2016)]. Using methods that control for joint cascade statistics, we find that for false- and true-news cascades, the reported structural differences can almost entirely be explained by false-news cascades being larger. For videos, images, news, and petitions, structural differences persist when controlling for size. Studying classical models of diffusion, we then give conditions under which differences in structural properties under different models do or do not reduce to differences in size. Our findings are consistent with the mechanisms underlying true- and false-news diffusion being quite similar, differing primarily in the basic infectiousness of their spreading process.

Misinformation: susceptibility, spread, and interventions to immunize the public

“…Although fake news can thus spread faster and deeper than true news, it is important to emphasize that these findings are based on a relatively narrow definition of fact-checked news (see Box 2 and ref. 70 ), and more recent research has pointed out that these estimates are likely platform-dependent 72 . Importantly, several studies have now shown that fake news typically represents a small part of people's overall media diet and that the spread of misinformation on social media is highly skewed so that a small number of accounts are responsible for the majority of the content that is shared and consumed, also known as 'supersharers' and 'superconsumers' 3,24,73 .…”

“…Measuring the infodemic. To return to the viral analogy, researchers have adopted models from epidemiology, such as the susceptible-Infected-recovered (SIR) model, to measure and quantify the spread of misinformation in online social networks 17,70 . In this context, R 0 often represents individuals who will start posting fake news following contact with someone who is already 'infected' .…”

Social and Cognitive Aspects of the Vulnerability to Political Misinformation

“…In addition, Friggeri et al (2014) find that corrections appearing online related to a specific unsubstantiated rumor can lead to a significant reduction in circulation. Juul and Ugander (2021) find that spreading behavior is similar for false and true news once the size of an information cascade is controlled for; the authors do find, however, that false‐news cascades are often larger. Thus, the evidence coming from the study of Internet data concerning the potential impact and magnitude of the misinformation problem for citizens is not more conclusive than the experimental evidence reviewed in the preceding sections.…”

Echo chambers and information transmission biases in homophilic and heterophilic networks

“…Information transmission in the context of Information and Communication Technologies is a great opportunity to create a better-informed society, but in practice these technologies are also promoting phenomena such as viral spreading of fake news [1][2][3] , echo chambers [4][5][6] , perception biases such as false consensus or majority illusions 7 , as well as social polarization 5,6,8 . We understand by echo chambers situations in which the transmission of information among individuals belonging to the same opinion group is dominant, while transmission among individuals with different opinions is hindered.…”