Spammers Are Becoming "smarter" on Twitter

Chen, C; Zhang, J; Xiang, Y; Zhou, W; Oliver, J

Spammers Are Becoming "smarter" on Twitter

Chen, C Zhang, J Xiang, Y Zhou, W

Oliver, J

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IT Professional, 2016, 18, (2), pp. 66-70
Issue Date:: 2016-03-01

Closed Access

	Filename	Description	Size
	Spammers_Are_Becoming_Smarter_on_Twitter.pdf	Published version	531.29 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chen, C
dc.contributor.author	Zhang, J
dc.contributor.author	Xiang, Y
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521
dc.contributor.author	Oliver, J
dc.date.accessioned	2022-01-30T22:23:40Z
dc.date.available	2022-01-30T22:23:40Z
dc.date.issued	2016-03-01
dc.identifier.citation	IT Professional, 2016, 18, (2), pp. 66-70
dc.identifier.issn	1520-9202
dc.identifier.uri	http://hdl.handle.net/10453/153882
dc.description.abstract	While researchers develop various approaches to detect Twitter spam, spammers thwart their efforts with more complex spamming strategies. The authors' in-depth analysis of more than 570 million tweets revealed three new spamming strategies: coordinated posting behavior, finite-state machine-based spam template, and passive spam.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IT Professional
dc.relation.isbasedon	10.1109/MITP.2016.36
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0806 Information Systems
dc.subject.classification	Information Systems
dc.title	Spammers Are Becoming "smarter" on Twitter
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-01-30T22:23:39Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	2

Abstract:

While researchers develop various approaches to detect Twitter spam, spammers thwart their efforts with more complex spamming strategies. The authors' in-depth analysis of more than 570 million tweets revealed three new spamming strategies: coordinated posting behavior, finite-state machine-based spam template, and passive spam.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/153882