The great number of social network users and the expansion of this kind of tool in the last years demand the storage of a great volume of information regarding user behaviour. In this article, we utilise interaction records from Facebook users and metrics from complex networks study, to identify different user behaviours using clustering techniques. We found three different user profiles regarding interactions performed in the social network: viewer, participant and content producer. Moreover, the groups we found were characterised by the C4.5 decision-tree algorithm. The 'viewer' mainly observes what happens in the network. The ‘participant’ interacts more often with the content, getting a higher value of closeness centrality. Therefore, users with a participant profile are responsible, for example, for the faster transmission of information in the virtual environment, a crucial function for the Facebook social network. We noted too that ‘content producer’ users had a greater quantity of publications in their pages, leading to a superior degree of input interactions than the other two profiles. Finally, we also verify that the profiles are not mutually exclusive, that is, the user of a profile can at determined moment perform the behaviour of another profile.

Formal concept analysis (FCA) is largely applied in different areas. However, in some FCA applications the volume of information that needs to be processed can become unfeasible. Thus, the demand for new approaches and algorithms that enable processing large amounts of information is increasing substantially. This article presents a new algorithm for extracting proper implications from high-dimensional contexts. The proposed algorithm, called ImplicPBDD, was based on the PropIm algorithm, and uses a data structure called binary decision diagram (BDD) to simplify the representation of the formal context and enhance the extraction of proper implications. In order to analyze the performance of the ImplicPBDD algorithm, we performed tests using synthetic contexts varying the number of objects, attributes and context density. The experiments show that ImplicPBDD has a better performance—up to 80% faster—than its original algorithm, regardless of the number of attributes, objects and densities.

Currently, several developers and companies design computational technologies aiming to influence the behavior of the users. However, many attempts of conceivingpersuasive technologies fail to comprehend which factors actually lead to a behavior change. In this article, we introducea novel behavorial model in the context of online social networks,based on Fogg's Behavior Model. The proposed model presentsthe behavior of social network users as a product of two factors: hability to accomplish a given target behavior and the presence of a trigger that leads to it.

Formal Concept Analysis (FCA) is an applied mathematical technique for data analysis, in which the relations between objects and attributes are identified. It introduces the notion of concepts and their hierarchical structure, from which we can obtain a set of implications between attributes that characterize a knowledge domain. The volume of information to be processed makes the use of FCA difficult in domains with a high number of dimensions, creating a demand for new solutions and algorithms for FCA applications. This article explores different approaches to extract proper implications from high dimensional contexts based on constraints to obtain the set of implications rules. We propose algorithms that use a data structure called Binary Decision Diagram (BDD) to represent the formal context, which reduces its size and, due to this, operates more efficiently. We also propose a heuristic to obtain proper implications by reducing the unnecessary generation of premises. In addition, we implemented a parallel computing model for generating and obtaining different implications. To analyze the proposed algorithms, we used different synthetic contexts with a varying number of objects, attributes, and density. The results obtained presented speed gains of up to 22 times when compared to the solutions proposed in the literature such as Impec and PropIm.