Abstract

A social circle is a category of strong social relationships, such as families, classmates and good friends and so on. The information diffusion among members of online social circles is frequent and credible. The research of users’ online social circles has become popular in recent years. Many scholars propose methods for detecting users’ online social circles. On the other hand, the social meanings and the tags of a social circle are also important for the analysis of a social circle. However, little work involves the tags discovery of social circles. This paper proposes an algorithm for social circle tag detection by multiple linear regression. The model solves the data sparse problem of tags in social circles and successfully combines different categories of features in social circles. We also redmap the concept of the social circle into "reference circles" of an academic paper. We evaluate our method in datasets of both Facebook and Microsoft Academic Search, and prove that it is more effective than other relevant methods.

Highlights

  • Social media is a popular communication platform

  • We propose an algorithm for detection of social circle tags via multiple linear regression

  • The lack of individuals’ tags means that this does not always work. For solving these problems of discovery of meaningful tags, we propose a model of multiple linear regression for detecting tags of social circles by combining features about the topological structure and the members’ tags of social circle

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Summary

Introduction

Social media is a popular communication platform. Compared with other information networks, user relationships promote more effective dissemination of information on social networks. There are different functional social medias, such as online academic networks, recommendation networks and social network services (SNS) and so on In these networks, users communicate with their friends and share information about their similar interests. Community detection is focused on finding arbitrary highly interconnected subgraphs within larger networks, and social circle detection will instead discover several groups of strong social relationships including one or more specific individuals [13]. According to these characteristics, detecting and analyzing user’s social circles is valuable for research on social network and user behavior. We conclude our work and point out avenues for future research

Social Circle
Tag Detection
Overview
Features
Multiple Linear Regression
Dataset
Baseline
Result Analysis
Findings
Conclusion
Full Text
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