Pregled bibliografske jedinice broj: 894256
Applying the Multiclass Classification Methods for the Classification of Online Social Network Friends
Applying the Multiclass Classification Methods for the Classification of Online Social Network Friends // 2017 International Conference on Software, Telecommunications and Computer Networks / Rožić, Nikola ; Lorenz, Pascal (ur.).
Split: Fakultet elektrotehnike, strojarstva i brodogradnje Sveučilišta u Splitu, 2017. (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 894256 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Applying the Multiclass Classification Methods for the Classification of Online Social Network Friends
Autori
Sever, Nikolina ; Humski, Luka ; Ilić, Juraj ; Skočir, Zoran ; Pintar, Damir ; Vranić, Mihaela
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2017 International Conference on Software, Telecommunications and Computer Networks
/ Rožić, Nikola ; Lorenz, Pascal - Split : Fakultet elektrotehnike, strojarstva i brodogradnje Sveučilišta u Splitu, 2017
ISBN
978-953-290-074-3
Skup
2017 International Conference on Software, Telecommunications and Computer Networks
Mjesto i datum
Split, Hrvatska, 21.09.2017. - 23.09.2017
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
online social networks, Facebook, multiclass classification, educational data mining
Sažetak
Online social networks (OSNs) are platforms which facilitate social interactions between their users through message exchange, photo and video sharing, status updates, etc. One of the most popular OSNs is Facebook. Connections between users on Facebook are modeled through concept of friendship. Each connection between users is binary – two users either are or aren't "friends". Information about of the actual intensity or nature of their connection is not available although in real life it can vary significantly. A majority of observed network friends are acquaintances in real-life while close friends are in the minority. The goal of this paper is to demonstrate and evaluate how user interaction statistics can be utilized for effective assessment of the nature of users' real-life relationship. Using an ensemble of popular classification algorithms, we will classify ego-user’s network friends into 3 groups: close friends, friends and acquaintances. As our main contribution, we will compare the efficiency of chosen algorithms and suggest the best approach for conducting this type of analysis on similar OSN communication data.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
