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Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing

Gross, Alexander and Murthy, Dhiraj. 2014. Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing. Neural Networks, N/A-N/A. ISSN 08936080 [Article]

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Abstract or Description

This paper explores a variety of methods for applying the Latent Dirichlet Allocation (LDA) automated topic modeling algorithm to the modeling of the structure and behavior of virtual organizations found within modern social media and social networking environments. As the field of Big Data reveals, an increase in the scale of social data available presents new challenges which are not tackled by merely scaling up hardware and software. Rather, they necessitate new methods and, indeed, new areas of expertise. Natural language processing provides one such method. This paper applies LDA to the study of scientific virtual organizations whose members employ social technologies. Because of the vast data footprint in these virtual platforms, we found that natural language processing was needed to ‘unlock’ and render visible latent, previously unseen conversational connections across large textual corpora (spanning profiles, discussion threads, forums, and other social media incarnations). We introduce variants of LDA and ultimately make the argument that natural language processing is a critical interdisciplinary methodology to make better sense of social ‘Big Data’ and we were able to successfully model nested discussion topics from forums and blog posts using LDA. Importantly, we found that LDA can move us beyond the state-of-the-art in conventional Social Network Analysis techniques.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1016/j.neunet.2014.05.008

Keywords:

Natural language processing; Latent Dirichlet Allocation; Big Data; Social media; Virtual organizations

Departments, Centres and Research Units:

Sociology

Dates:

DateEvent
2014Published

Item ID:

10390

Date Deposited:

16 Jun 2014 08:40

Last Modified:

29 Apr 2020 15:59

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/10390

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