Enhancing Social Network Analysis with a Concept-Based Text Mining Approach to Discover Key Members on a Virtual Community of Practice

Álvarez, H.; Ríos, Sebastián A.; Aguilera, Felipe; Merlo, Eduardo; Guerrero, Luis A.

doi:10.1007/978-3-642-15390-7_61

Cited by 19 publications

(30 citation statements)

References 8 publications

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“…Our evaluation was based on interviews with the administrators plus a preliminary study of community activity. This was presented in [4], [5]. However, in this paper we use our semantic representation (sioc-sna-dm) to reconstruct the same experiments to probe that the model supports social network analysis and also data mining on a VCoP which is a specialized (more restricted) social network.…”

Section: Application To Plexilandia Communitymentioning

confidence: 96%

“…based on relationships between community members [2], [3]. Others prefer to use data mining, especially web mining (WM), where the structure of social interactions is lost but it is possible to find interesting patterns of texts in members posts or navigation patterns [4], [5], [6].…”

Section: Related Workmentioning

confidence: 99%

“…Similarly [4] developed an LDA-driven process to extract meaningful topics for members of a virtual community of practice in order to discover for example hidden keymembers.…”

Section: A Efforts To Add Semantics Into Social Network Analysismentioning

confidence: 99%

“…Therefore, we obtained a density reduction of about 80% and we kept the quality of traditional methods results (as shown in [4], [5]). Besides, since we also store intrinsic topics information, we can also enhance other algorithms outputs, as we will show in the next sections.…”

Section: B Extracting Filtered Network From Sioc-sna-dmmentioning

confidence: 99%

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Leveraging Social Network Analysis with Topic Models and the Semantic Web

Rios

Aguilera

Bustos

et al. 2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

Self Cite

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Abstract-Social Network Analysis (SNA) and Web Mining (WM) techniques are being applied to study the structures of social networks in order to manage their dynamics and predict their evolution. This paper describes how we used Semantically-Interlinked Online Communities (SIOC) ontology to represent the (latent) semantic relationships between the members of a large community forum (about 2,500), Plexilandia. We extended SIOC, taking advantage of topic-based text mining and developed data mining algorithms that used our SIOC extensions to provide a better understanding of the social dynamics of the members of the Plexilandia community. This new understanding helped us to detect and discover the key members of Plexilandia successfuly.

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Section: Application To Plexilandia Communitymentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

“…Similarly [4] developed an LDA-driven process to extract meaningful topics for members of a virtual community of practice in order to discover for example hidden keymembers.…”

Section: A Efforts To Add Semantics Into Social Network Analysismentioning

confidence: 99%

Section: B Extracting Filtered Network From Sioc-sna-dmmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging Social Network Analysis with Topic Models and the Semantic Web

Rios

Aguilera

Bustos

et al. 2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

Self Cite

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“…possible alters), and moved those rows to a separate worksheet. Question and answer pairing has been shown to be an effective means to construct a social network structure for e-mail based communication (Kwong and Yorke-Smith, 2012) and Internet forum posts (Alvarez et al, 2010;Hong and Davidson, 2009;Sun et al, 2010).…”

Section: Interpretive Frameworkmentioning

confidence: 99%

Detecting Covert Networks in Multilingual Groups: Evidence within a Virtual World

Triplet¹,

Harrison²,

Mennecke³

et al. 2016

JVWR

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This paper introduces an approach for the examination and organization of unstructured text to identify relationships between networks of individuals. This approach uses discourse analysis to identify information providers and recipients and determines the structure of covert organizations irrespective of the language that facilitate conversations between members. Then, this method applies social network analytics to determine the arrangement of a covert organization without any a priori knowledge of the network structure. This approach is tested and validated using communication data collected in a virtual world setting. Our analysis indicates that the proposed framework successfully detected the covert structure of three information networks, and their cliques, within an online gaming community during a simulation of a large-scale event.

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