Online social networks (OSN) are one of the most popular forms of modern
communication and among the best known is Facebook. Information about the
connection between users on the OSN is often very scarce. It's only known if
users are connected, while the intensity of the connection is unknown. The aim
of the research described was to determine and quantify friendship intensity
between OSN users based on analysis of their interaction. We built a
mathematical model, which uses: supervised machine learning algorithm Random
Forest, experimentally determined importance of communication parameters and
coefficients for every interaction parameter based on answers of research
conducted through a survey. Taking user opinion into consideration while
designing a model for calculation of friendship intensity is a novel approach
in opposition to previous researches from literature. Accuracy of the proposed
model was verified on the example of determining a better friend in the offered
pair
