###### 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. community formation; filter bubble; echo chamber; recommendation systems; social media; agent-based simulations +
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0000-0001-7720-0822][PERSON]  
0000-0001-7720-0822][PERSON]  
0000-0001-7720-0822]G.S. Social engineering methods are based on the weaknesses of our decision systems [5] that are essentially related to the dual process theory, one being an implicit (automatic), unconscious process, and the other an explicit (controlled), conscious one. The first process is based on heuristics, i.e., \"rules of thumb\" based on previous experiences and easy to retrieve, while the second consists in rational thinking. The first process is preferred unless there is a strong reason to check the rationality of the behavior, and is strongly promoted in situations of danger, stress, and/or bounded time limit. These situations are typically enforced by vendors and scammers. Social engineering often consists in the participation in active discussions, interviews, focus groups, etc. These two methods require a certain amount of human work. Content-based systems use the classification of the characteristics of personal information disclosed by users. An improvement of this latter method is given by collaborative filtering, which is based on the harnesses of information that can be gathered from users' communications (e.g., browsing history or other pieces of interaction). This method extracts knowledge from transactions (that will be addressed in the following as \"posts\", having in mind an exchange of messages on social media), and stores them in a database, to be confronted with the ones of other users. Using the stored information on the reading or active evaluation (\"likes\") of users on other users' posts, collaborative filtering can extrapolate information regarding the similarities among those users and recommend unread posts only of those which the system believes to be similar. The same techniques obviously apply to e-commerce and advertising. One possible drawback of this kind of recommendation system is the formation of filter bubbles [6; 7] or echo chambers [8; 9], i.e., the formation of intellectually closed circles of users that exchange information only about selected topics or share the same opinion.