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https://doi.org/10.17559/TV-20170124144723

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

Sanja Krakan orcid id orcid.org/0000-0002-0560-6760 ; University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia
Luka Humski orcid id orcid.org/0000-0002-6819-8899 ; University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia
Zoran Skočir ; University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia


Puni tekst: engleski pdf 910 Kb

str. 655-662

preuzimanja: 678

citiraj


Sažetak

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 is 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.

Ključne riječi

Facebook, machine learning, mathematical model, online social network, random forest, supervised learning, tie strength, user interaction

Hrčak ID:

202564

URI

https://hrcak.srce.hr/202564

Posjeta: 1.490 *