A Comparative Analysis of Social Network Pages by Interests of Their Followers
This work addresses cross-lingual and cross-platform interest classification for social media analysis, but it is incremental as it applies existing methods to new data.
The study tackled the problem of classifying social network pages by user interests across languages and platforms, achieving the highest classification score with English Twitter pages.
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.