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Social information landscapes: automated mapping of large multimodal, longitudinal social networks

Ch'ng, Eugene

Authors

Eugene Ch'ng



Abstract

Purpose – This article presents a Big Data solution as a methodological approach to the automated collection, cleaning, collation and mapping of multimodal, longitudinal datasets from social media. The article constructs Social Information Landscapes.
Design/methodology/approach – The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages and other forms of media, so long as a formal data structure proposed here can be constructed.
Findings – The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on Web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g., centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating and mapping such datasets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.
Research limitations/implications – The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this article is to demonstrate the feasibility of using automated approaches for acquiring massive, connected datasets for academic inquiry in the social sciences.
Practical implications – The methods presented in this article, and the Big Data architecture presented here have great practical values to individuals and institutions which have low budgets. The software-hardward integrated architecture uses open source software, and the social information landscapes mapping algorithms are not difficult to implement.
Originality/value – The majority of research in the literatures uses traditional approach for collecting social networks data. The traditional approach is slow, tedious and does not yield a large enough sample for the data to be significant for analysis. Whilst traditional approach collects only a small percentage of data, the original methods presented could possibility collect entire datasets in social media due to its scalability and automated mapping techniques.

Journal Article Type Article
Publication Date Sep 10, 2015
Journal Industrial Management & Data Systems
Electronic ISSN 0263-5577
Publisher Emerald
Peer Reviewed Peer Reviewed
Volume 115
Issue 9
APA6 Citation Ch'ng, E. (2015). Social information landscapes: automated mapping of large multimodal, longitudinal social networks. Industrial Management and Data Systems, 115(9), https://doi.org/10.1108/imds-02-2015-0055
DOI https://doi.org/10.1108/imds-02-2015-0055
Keywords Social network analysis, big data, mapping, online communities, social media; multimodal network;
longitudinal network
Publisher URL http://www.emeraldinsight.com/doi/full/10.1108/IMDS-02-2015-0055
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Eugene Ch'ng, (2015) "Social information landscapes: Automated mapping of large multimodal, longitudinal social networks", Industrial Management & Data Systems, Vol. 115 Issue: 9, pp.1724-1751, https://doi.org/10.1108/IMDS-02-2015-0055

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf



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