Approximate Contagion Model of Common Knowledge on Facebook

Abstract

Computational modeling of information exchange over social media is important for understanding coordination in online environments. Many contagion dynamics models that have been used to model Twitter, Facebook, and blog information transmission are polynomial-time computable, and hence can be efficiently simulated on networked populations. Game-theoretic models of collective action (i.e., coordination problems that require common knowledge among agents), however, have dynamics that are controlled in part by specific network structures such as cliques and bicliques. Contagion dynamics with these models cannot be efficiently computed because finding all bicliques in a graph, for example, is an NP-hard problem. We investigate a recent model of common knowledge dynamics that represents information spread on Facebook-in which the biclique is the characterizing structure-and convert the model into an efficiently computable one by using an approximation. We demonstrate this through experiments on seven different graphs for a total of 168 sets of conditions, including a 4-order of magnitude span in dynamics parameter values. Our approach speeds computations in two ways: (i) it obviates the need to find all bicliques in a social network, which is a very timeconsuming task (computations can take 30 to 120 hours or more of wall clock time), and (ii) it reduces the time of simulation computations, in some cases by well over an order of magnitude. Our method also enables evaluation of much larger networks that are being mined from social media.

Collective action in social entrepreneurial ventures: the role of shared meaning

“…There are numerous groups that operate in slums that take advantage of the locals, which raises questions about whether those who organise the neighborhood for collective action are doing so for their own personal gain. This gives the impression that the CBO teams are using the organisations as a means of achieving personal objectives rather than resolving issues for the benefit of all parties (Korkmaz, 2018). From the above explanations, information exchange by CBOs did not always result in joint action.…”

Analytical study of quality-biased competition dynamics for memes in social media

“…-Understanding how information spreads in social media is a topic of uttermost interest, as it is fundamental for devising strategies aimed at fostering the diffusion of beneficial information or contrasting the dangerous spread of fake news [1][2][3]. Activity in this area has boomed in recent years [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. From the point of view of statistical physics, information spreading is a prominent example of a collective macroscopic phenomenon emerging in a self-organized manner from the spontaneous activity of a large number of individual elements [20,21].…”