The "Majority Illusion" in Social Networks

Abstract

Individual’s decisions, from what product to buy to whether to engage in risky behavior, often depend on the choices, behaviors, or states of other people. People, however, rarely have global knowledge of the states of others, but must estimate them from the local observations of their social contacts. Network structure can significantly distort individual’s local observations. Under some conditions, a state that is globally rare in a network may be dramatically over-represented in the local neighborhoods of many individuals. This effect, which we call the “majority illusion,” leads individuals to systematically overestimate the prevalence of that state, which may accelerate the spread of social contagions. We develop a statistical model that quantifies this effect and validate it with measurements in synthetic and real-world networks. We show that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.

Temporal pattern of online communication spike trains in spreading a scientific rumor: how often, who interacts with whom?

“…The study of these digital footprints has led to the emergence of computational social science, allowing to quantify at large-scales our political ideas and preferences [1], to discover roles in social networks [2,3], to predict our health [4] and personality [5], and to determine external effects on online behavior [6]. Importantly, in OSM, users are at the same time both actors and receivers and therefore the amplification of a trend originates from the interplay between influencing [7,8] and being influenced [9][10][11][12][13].…”

Vaping on Instagram: cloud chasing, hand checks and product placement

“…The images of ‘cloud chasing’ could contribute to the normalisation of vaping, characterising the act as harmless and fun, and more common and accepted than it actually is offline. 21 Research has consistently demonstrated the strong effect of peer influence and perceptions of peer norms on combustible cigarette use. 2223 However, tobacco education programmes have successfully combatted the effects of peer influence by addressing the associated social norms, 2425 a strategy that could be implemented on social media.…”

Major Mechanisms Contributing to Swarm Intelligence

“…It should surely be each individual reader who independently judges the worth of that opinion and this individual should not be swayed by the popular and well connected person or group who posted it. Otherwise a dangerous or hateful opinion can spread so far that it becomes a ‘majority illusion’ (Lerman et al ., ).…”

“…It should surely be each individual reader who independently judges the worth of that opinion and this individual should not be swayed by the popular and well connected person or group who posted it. Otherwise a dangerous or hateful opinion can spread so far that it becomes a 'majority illusion' (Lerman et al, 2015). The type of natural selection of memes found in the swarm can be characterized as a non intentional selection process (Hull, 1988) in which the differential survival of alternative nest site propositions identifies the best competing alternatives.…”