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2018 | OriginalPaper | Buchkapitel

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

verfasst von : Elena Mikhalkova, Yuri Karyakin, Igor Glukhikh

Erschienen in: Computational Science – ICCS 2018

Verlag: Springer International Publishing

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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.

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Model Texts are characterised by intense communication of multiple representatives of a social group and are often quite large. But if the text is too large, it becomes noisy. Empirically we found out that selection of approximately 1,000 features is most effective. However, this observation requires more research. Also, we observed that some Model Texts provide better results than other; often using two texts instead of one is more effective. However, unlike common supervised learning algorithms, with the increase in the number of Model Texts (even up to 10–15) our algorithm becomes less efficient.

These are conditions similar to what our algorithm requires. To extract features, it needs one or two Model Texts and a couple of non-class texts to extract uniques.

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Titel: Large Scale Retrieval of Social Network Pages by Interests of Their Followers
verfasst von: Elena Mikhalkova
Yuri Karyakin
Igor Glukhikh
Verlag: Springer International Publishing
Buch: Computational Science – ICCS 2018
Print ISBN: 978-3-319-93697-0

Electronic ISBN: 978-3-319-93698-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93698-7_18

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