A Comparative Analysis of Social Network Pages by Interests of Their Followers

TL;DR

This study investigates whether user interests can be classified across different social networks and languages by analyzing Twitter and Vkontakte pages, demonstrating that language and platform influence classification accuracy.

Contribution

The paper introduces a model of Major Interests and compares classification performance across languages and platforms using machine learning algorithms.

Findings

Russian-Vkontakte and Russian-Twitter pages show higher correlation.

English-Twitter pages achieve the highest classification scores.

Interest classification is effective across different social networks and languages.

Abstract

Being a matter of cognition, user interests should be apt to classification independent of the language of users, social network and content of interest itself. To prove it, we analyze a collection of English and Russian Twitter and Vkontakte community pages by interests of their followers. First, we create a model of Major Interests (MaIs) with the help of expert analysis and then classify a set of pages using machine learning algorithms (SVM, Neural Network, Naive Bayes, and some other). We take three interest domains that are typical of both English and Russian-speaking communities: football, rock music, vegetarianism. The results of classification show a greater correlation between Russian-Vkontakte and Russian-Twitter pages while English-Twitterpages appear to provide the highest score.