Large Scale Retrieval of Social Network Pages by Interests of Their Followers

Elena Mikhalkova, Yuri Karyakin, Igor Glukhikh

Abstract: Social networks provide an opportunity to form communities of people that share their interests on a regular basis (circles of fans of different music, books, kinds of sports, etc.). Every community manifests these interests creating lots of linguistic data to attract new followers to certain pages and support existing clusters of users. In the present article, we suggest a model of retrieving such pages that attract users with similar interests, from a large collection of pages. We test our model on three types of pages manually retrieved from the social network Vkontakte and classified as interesting for a. football fans, b. vegetarians, c. historical reenactors. We use such machine learning classifiers as Naive Bayes, SVM, Logistic Regression, Decision Trees to compare their performance with the performance of our system. It appears that the mentioned classifiers can hardly retrieve (i.e. single out) pages with a particular interest that form a small collection of 30 samples from a collection as large as 4,090 samples. In particular, our system exceeds their best result (F1-score = 0.65) and achieves F1-score of 0.72.