Community Topical “Fingerprint” Analysis Based on Social Semantic Networks

  • Dongsheng Wang
  • Kyunglag Kwon
  • Jongsoo Sohn
  • Bok-Gyu Joo
  • In-Jeong Chung
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)

Abstract

Community analysis of social networks is a widely used technique in many fields. There have been many studies on community detection where the detected communities are attached to a single topic. However, an overall topical analysis for a community is required since community members are often concerned with multiple topics. In this paper, we propose a semantic method to analyze the topical community “fingerprint” in a social network. We represent the social network data as an ontology, and integrate with two other ontologies, creating a Social Semantic Network (SSN) context. Then, we take advantage of previous topological algorithms to detect the communities and retrieve the topical “fingerprint” using SPARQL. We extract about 210,000 Twitter profiles, detect the communities, and demonstrate the topical “fingerprint”. It shows human-friendly as well as machine-readable results, which can benefit us when retrieving and analyzing communities according to their interest degrees in various domains.

Keywords

Community Topical fingerprint Community detection Social semantic network (SSN) Semantic network (SN) 

Notes

Acknowledgment

This research was partially supported by Korea University.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Dongsheng Wang
    • 1
  • Kyunglag Kwon
    • 1
  • Jongsoo Sohn
    • 2
  • Bok-Gyu Joo
    • 3
  • In-Jeong Chung
    • 1
  1. 1.Department of Computer and Information ScienceKorea UniversitySeoulKorea
  2. 2.Service Strategy Team, Visual DisplaySamsung ElectronicsSuwonKorea
  3. 3.Department of Computer and Information CommunicationsHong-Ik UniversitySeoulKorea

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