Mitigating the Impact of Data Sampling on Social Media Analysis and Mining

Xu, K; Wang, F; Wang, H; Wang, Y; Zhang, Y

Mitigating the Impact of Data Sampling on Social Media Analysis and Mining

Xu, K Wang, F Wang, H Wang, Y Zhang, Y

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computational Social Systems, 2020, 7, (2), pp. 546-555
Issue Date:: 2020-04-01

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Field	Value	Language
dc.contributor.author	Xu, K
dc.contributor.author	Wang, F
dc.contributor.author	Wang, H
dc.contributor.author	Wang, Y
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638
dc.date.accessioned	2020-11-03T00:49:29Z
dc.date.available	2020-11-03T00:49:29Z
dc.date.issued	2020-04-01
dc.identifier.citation	IEEE Transactions on Computational Social Systems, 2020, 7, (2), pp. 546-555
dc.identifier.issn	2373-7476
dc.identifier.issn	2329-924X
dc.identifier.uri	http://hdl.handle.net/10453/143701
dc.description.abstract	© 2014 IEEE. The last decade has witnessed the explosive growth of online social media in users and contents. Due to the unprecedented scale and the cascading power of the underlying social networks, social media has created a new paradigm for sharing information, broadcasting breaking news, and reporting real-time events by any user from anywhere at any time. Many popular social media sites including Twitter provide streaming data services by standard APIs to the broad researcher and developer communities. Given the sheer data volume, rapid velocity, and feature variety of online social media, these sites often supply only a sampled set of streaming data, rather than the full data set to reduce the resource cost of computations, storage, and network bandwidth. In light of the substantial impact of sampling in Twitter data stream, this article explores a combination of spectral clustering, locality-sensitive hashing (LSH), latent Dirichlet allocation (LDA) topic modeling, and differential equation modeling to mitigate the impact of sampling on social media data analysis, in particular on detecting real-world events and predicting information diffusion. Our extensive experiments demonstrate that our proposed method is able to detect effectively the real-time emerging events and predict accurately the cascading pattern of these events from the 1% sampled Twitter data stream. To the best of our knowledge, this article is the first effort to introduce a systematic methodology to study and mitigate the impact of data sampling on social media analysis and mining.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Computational Social Systems
dc.relation.isbasedon	10.1109/TCSS.2020.2970602
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Mitigating the Impact of Data Sampling on Social Media Analysis and Mining
dc.type	Journal Article
utslib.citation.volume	7
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-03T00:49:23Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	2

Abstract:

© 2014 IEEE. The last decade has witnessed the explosive growth of online social media in users and contents. Due to the unprecedented scale and the cascading power of the underlying social networks, social media has created a new paradigm for sharing information, broadcasting breaking news, and reporting real-time events by any user from anywhere at any time. Many popular social media sites including Twitter provide streaming data services by standard APIs to the broad researcher and developer communities. Given the sheer data volume, rapid velocity, and feature variety of online social media, these sites often supply only a sampled set of streaming data, rather than the full data set to reduce the resource cost of computations, storage, and network bandwidth. In light of the substantial impact of sampling in Twitter data stream, this article explores a combination of spectral clustering, locality-sensitive hashing (LSH), latent Dirichlet allocation (LDA) topic modeling, and differential equation modeling to mitigate the impact of sampling on social media data analysis, in particular on detecting real-world events and predicting information diffusion. Our extensive experiments demonstrate that our proposed method is able to detect effectively the real-time emerging events and predict accurately the cascading pattern of these events from the 1% sampled Twitter data stream. To the best of our knowledge, this article is the first effort to introduce a systematic methodology to study and mitigate the impact of data sampling on social media analysis and mining.

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http://hdl.handle.net/10453/143701