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Measuring the Topical Specificity of Online Communities
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Abstract For community managers and hosts it is not only important to identify the current key topics of a community but also to assess the specificity level of the community for: a) creating sub-communities, and: b) anticipating community behaviour and topical evolution. In this paper we present an approach... view more
For community managers and hosts it is not only important to identify the current key topics of a community but also to assess the specificity level of the community for: a) creating sub-communities, and: b) anticipating community behaviour and topical evolution. In this paper we present an approach that empirically characterises the topical specificity of online community forums by measuring the abstraction of semantic concepts discussed within such forums. We present a range of concept abstraction measures that function over concept graphs - i.e. resource type-hierarchies and SKOS category structures - and demonstrate the efficacy of our method with an empirical evaluation using a ground truth ranking of forums. Our results show that the proposed approach outperforms a random baseline and that resource type-hierarchies work well when predicting the topical specificity of any forum with various abstraction measures.... view less
Keywords
internet community; measurement; topicality; social media; Ireland; comparison of methods; experiment; algorithm
Classification
Natural Science and Engineering, Applied Sciences
Interactive, electronic Media
Free Keywords
Online Community; Composite Function; Type Graph; Eigenvector Centrality; Concept Graph; Boards.ie
Collection Title
The Semantic Web: Semantics and Big Data; 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013: Proceedings
Editor
Cimiano, Philipp; Corcho, Oscar; Presutti, Valentina; Hollink, Laura; Rudolph, Sebastian
Conference
10. The Semantic Web: Semantics and Big Data (ESWC 2013). Montpellier, 2013
Document language
English
Publication Year
2013
Publisher
Springer
City
Berlin
Page/Pages
p. 472-486
Series
Lecture Notes in Computer Science (LNCS), 7882
DOI
https://doi.org/10.1007/978-3-642-38288-8_32
ISSN
1611-3349
ISBN
978-3-642-38288-8
Status
Published Version; peer reviewed
Licence
Deposit Licence - No Redistribution, No Modifications