The value of using big data technologies in computational social science

Ch'ng, Eugene (2014) The value of using big data technologies in computational social science. In: 2014 International Conference on Big Data Science and Computing, 4 -7 August 2014, Beijing, China.

[img] PDF - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (383kB)

Abstract

The discovery of phenomena in social networks has prompted renewed interests in the field. Data in social networks however can be massive, requiring scalable Big Data architecture. Conversely, research in Big Data needs the volume and velocity of social media data for testing its scalability. Not only so, appropriate data processing and mining of acquired datasets involve complex issues in the variety, veracity, and variability of the data, after which visualisation must occur before we can see fruition in our efforts. This article presents topical, multimodal, and longitudinal social media datasets from the integration of various scalable open source technologies. The article details the process that led to the discovery of social information landscapes within the Twitter social network, highlighting the experience of dealing with social media datasets, using a funneling approach so that data becomes manageable. The article demonstrated the feasibility and value of using scalable open source technologies for acquiring massive, connected datasets for research in the social sciences.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in Proceedings of the 2014 International Conference on Big Data Science and Computing, Article no. 6, Beijing, China, 4-7 Augusht 2014. New York : ACM, 2014. ISBN: 978-1-4503-2891-3.
Keywords: social network analysis; computational social science; data mining; open source;twitter
Schools/Departments: University of Nottingham Ningbo China > Faculty of Humanities and Social Sciences > School of International Communications
Identification Number: https://doi.org/10.1145/2640087.2644162
Related URLs:
URLURL Type
https://dl.acm.org/citation.cfm?id=2644162Organisation
Depositing User: Wu, Cocoa
Date Deposited: 20 Feb 2019 11:36
Last Modified: 20 Feb 2019 11:36
URI: http://eprints.nottingham.ac.uk/id/eprint/56024

Actions (Archive Staff Only)

Edit View Edit View