Determination of Friendship Intensity between Online Social Network Users Based on Their Interaction

TL;DR

This paper presents a novel mathematical model using supervised machine learning to quantify friendship intensity between OSN users based on their interactions, incorporating user opinions for improved accuracy.

Contribution

It introduces a new approach that integrates user survey data into a machine learning model to better estimate friendship strength in social networks.

Findings

Model achieved high accuracy in identifying closer friends.

Communication parameters' importance was experimentally determined.

User opinions improved model reliability.

Abstract

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.