Community Formation as a Byproduct of a Recommendation System: A Simulation Model for Bubble Formation in Social Media

Franco Bagnoli, Guido de Bonfioli Cavalcabo’, Banedetto Casu and Andrea Guazzini
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Franco Bagnoli: Department of Physics and Astronomy and CSDC, University of Florence, Via G. Sansone 1, 50019 Sesto Fiorentino, Italy
Guido de Bonfioli Cavalcabo’: Department of Physics and Astronomy and CSDC, University of Florence, Via G. Sansone 1, 50019 Sesto Fiorentino, Italy
Banedetto Casu: Department of Physics and Astronomy and CSDC, University of Florence, Via G. Sansone 1, 50019 Sesto Fiorentino, Italy
Andrea Guazzini: Department of Education, Languages, Intercultures, Literatures and Psychology and CSDC, University of Florence, Via Laura 48, 50121 Firenze, Italy

Future Internet, 2021, vol. 13, issue 11, 1-11

Abstract: We investigate the problem of the formation of communities of users that selectively exchange messages among them in a simulated environment. This closed community can be seen as the prototype of the bubble effect, i.e., the isolation of individuals from other communities. We develop a computational model of a society, where each individual is represented as a simple neural network (a perceptron), under the influence of a recommendation system that honestly forward messages (posts) to other individuals that in the past appreciated previous messages from the sender, i.e., that showed a certain degree of affinity. This dynamical affinity database determines the interaction network. We start from a set of individuals with random preferences (factors), so that at the beginning, there is no community structure at all. We show that the simple effect of the recommendation system is not sufficient to induce the isolation of communities, even when the database of user–user affinity is based on a small sample of initial messages, subject to small-sampling fluctuations. On the contrary, when the simulated individuals evolve their internal factors accordingly with the received messages, communities can emerge. This emergence is stronger the slower the evolution of individuals, while immediate convergence favors to the breakdown of the system in smaller communities. In any case, the final communities are strongly dependent on the sequence of messages, since one can get different final communities starting from the same initial distribution of users’ factors, changing only the order of users emitting messages. In other words, the main outcome of our investigation is that the bubble formation depends on users’ evolution and is strongly dependent on early interactions.

Keywords: community formation; filter bubble; echo chamber; recommendation systems; social media; agent-based simulations (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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