Personalidade e redes sociais: agrupando e analisando características comportamentais de usuários de redes sociais a partir da combinação de traços de personalidade, dados demográficos e pegadas digitais

Abstract

Digital social networks are becoming more mainstream, offering a massive platform for analyzing human behavior in computer-mediated contexts. Algorithms can explore human behavior by analyzing digital footprints left by people when interacting with social networks. Digital footprints can be produced actively (in a consenting way) and passively (unintentionally). It is through them that studies can explore behavior and social interaction on a large scale. Thus, the discovery of essential and valuable information from digital footprints left on social networks is carried out using pattern recognition technologies and statistical and mathematical techniques; this discipline is referred to as data mining. This research seeks to identify user profiles in social networks by grouping behavior data in social networks (digital footprints), demographic data, and socio-affective profiles (personality traits). More specifically, unsupervised machine learning algorithms (clustering) such as K-means and Spectral Clustering are applied. Unlike other works on personality detection on social networks, the proposed work explores clustering techniques to group users with similar profiles by collecting their digital footprints, demographic data, and personality traits. From there, that work aims to understand the personality manifestations of social network users through their behavior, i.e, the role that different personalities play in the behavior of users on social networks. Although this work analyzes a small group of users (157 participants), some correlations observed in the related bibliography could be found. That work a first step for future incremental works in order to raise awareness about the relationship of social networks, Personality Computation and the several underlying fields related to strictly personal and sensitive data. This research also brings as a contribution a new set of labeled and high-dimensional data (a database), which combine behavioral data with characteristics extracted from active and passive digital footprints, personality and demographic information from a social network in Portuguese.