Especificação de um framework para definir o perfil dos usuários através das interações sociais - Pereira, William Bortoluzzi (2019)
The social context is an important factor for Context-Aware Computing Applications since it has the capacity to make decisions, considering the current state of the individual (his current context). A fundamental element for the recognition of the social context of the user is based on their social relations, these relations are mediated through a set of social interactions. Social interactions can occur in both the virtual and face-to-face environments, and depend on the context in which the user is currently at. The present work aims to identify and analyze the integration and involvement of individuals from their social interactions face to face with the use of a mobile application. This paper aims to identify and analyze the integration and involvement of individuals from their face-to-face social interactions with the use of a mobile device application. The challenge found in this area is the use of concepts that until now were considered abstract within the computational environment, concepts such as social context, social relation and social interaction, which are frequently used in areas such as Medicine, Social Science, Psychology and Humanistics. With this was developed the Framework Semantic Social Count (SSC), this presents several concepts that reach a goal, which is to de ne a user pro le from their interactions. From the Framework an application was developed uniting the set of approaches studied, the application was named "InterApp". The data captured by the application are used to establish user pro les using social integration indicators and their respective acceptance and rejection indices within the social environment of individuals, making it possible to identify the interactions between individuals in a quantitative way, being possible to observe who each user interacted with and how often; It was also possible to produce social pro les of individuals with qualitative character, obtaining 54.29% of the evaluated interactions recorded as positive, these were chosen from the results obtained by the application, attributing semantics to the data captured.