Community topical fingerprint analysis based on social semantic networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, D. | - |
dc.contributor.author | Kwon, K. | - |
dc.contributor.author | Sohn, J. | - |
dc.contributor.author | Joo, B.-G. | - |
dc.contributor.author | Chung, I.-J. | - |
dc.date.accessioned | 2021-10-12T08:43:51Z | - |
dc.date.available | 2021-10-12T08:43:51Z | - |
dc.date.created | 2021-10-12 | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 1876-1100 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/16395 | - |
dc.description.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. © Springer Science+Business Media Dordrecht 2014. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.subject | Community | - |
dc.subject | Community detection | - |
dc.subject | Semantic network | - |
dc.subject | Social semantics | - |
dc.subject | Topical fingerprint | - |
dc.subject | Population dynamics | - |
dc.subject | Semantics | - |
dc.subject | Social networking (online) | - |
dc.subject | Sanitary sewers | - |
dc.title | Community topical fingerprint analysis based on social semantic networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Joo, B.-G. | - |
dc.identifier.doi | 10.1007/978-94-007-7262-510 | - |
dc.identifier.scopusid | 2-s2.0-84893793646 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Electrical Engineering, v.260 LNEE, pp.83 - 91 | - |
dc.relation.isPartOf | Lecture Notes in Electrical Engineering | - |
dc.citation.title | Lecture Notes in Electrical Engineering | - |
dc.citation.volume | 260 LNEE | - |
dc.citation.startPage | 83 | - |
dc.citation.endPage | 91 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Community | - |
dc.subject.keywordPlus | Community detection | - |
dc.subject.keywordPlus | Semantic network | - |
dc.subject.keywordPlus | Social semantics | - |
dc.subject.keywordPlus | Topical fingerprint | - |
dc.subject.keywordPlus | Population dynamics | - |
dc.subject.keywordPlus | Semantics | - |
dc.subject.keywordPlus | Social networking (online) | - |
dc.subject.keywordPlus | Sanitary sewers | - |
dc.subject.keywordAuthor | Community | - |
dc.subject.keywordAuthor | Community detection | - |
dc.subject.keywordAuthor | Semantic network (SN) | - |
dc.subject.keywordAuthor | Social semantic network (SSN) | - |
dc.subject.keywordAuthor | Topical fingerprint | - |
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