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Advanced Technologies, Embedded and Multimedia for Human-centric Computing pp 83–91Cite as

Community Topical “Fingerprint” Analysis Based on Social Semantic Networks

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Part of the book series: Lecture Notes in Electrical Engineering ((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.

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Notes

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Acknowledgment

This research was partially supported by Korea University.

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Korea University, Seoul, Korea

    Dongsheng Wang, Kyunglag Kwon & In-Jeong Chung

  2. Service Strategy Team, Visual Display, Samsung Electronics, Suwon, Korea

    Jongsoo Sohn

  3. Department of Computer and Information Communications, Hong-Ik University, Seoul, Korea

    Bok-Gyu Joo

Authors
  1. Dongsheng Wang
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  2. Kyunglag Kwon
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  3. Jongsoo Sohn
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  4. Bok-Gyu Joo
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  5. In-Jeong Chung
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Corresponding author

Correspondence to Dongsheng Wang .

Editor information

Editors and Affiliations

  1. ES Department, National Cheng Kung University, Tainan City, Taiwan

    Yueh-Min Huang

  2. Engineering, National Ilan University Inst. Computer Science & Information, I-Lan, Taiwan

    Han-Chieh Chao

  3. Dept of Computer Sci. and Inf. Bao-Shan Campus, National Changhua University of Education, Changhua City, Taiwan

    Der-Jiunn Deng

  4. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

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© 2014 Springer Science+Business Media Dordrecht

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Cite this paper

Wang, D., Kwon, K., Sohn, J., Joo, BG., Chung, IJ. (2014). Community Topical “Fingerprint” Analysis Based on Social Semantic Networks. In: Huang, YM., Chao, HC., Deng, DJ., Park, J. (eds) Advanced Technologies, Embedded and Multimedia for Human-centric Computing. Lecture Notes in Electrical Engineering, vol 260. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7262-5_10

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  • DOI: https://doi.org/10.1007/978-94-007-7262-5_10

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-7261-8

  • Online ISBN: 978-94-007-7262-5

  • eBook Packages: EngineeringEngineering (R0)

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