Collective Hyping Detection System for Identifying Online Spam Activities

Zhang, Q; Wu, J; ZHANG, P; Long, G; Zhang, C

Collective Hyping Detection System for Identifying Online Spam Activities

Zhang, Q Wu, J ZHANG, P Long, G Zhang, C

Permalink

Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2017
Issue Date:: 2017-06-14

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted ManuscriptAdobe PDF (1.35 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, Q	en_US
dc.contributor.author	Wu, J	en_US
dc.contributor.author	ZHANG, P	en_US
dc.contributor.author	Long, G	en_US
dc.contributor.author	Zhang, C	en_US
dc.date.issued	2017-06-14	en_US
dc.identifier.citation	IEEE Intelligent Systems, 2017	en_US
dc.identifier.issn	1541-1672	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117999
dc.description.abstract	IEEE Online reviews are extensively utilized by potential buyers to make business decisions. Unfortunately, fraudsters offer to write spam reviews for product promotion or competitor defamation, which drives online business holders to adopt this type of vicious strategy to increase their profits. These fake reviews always mislead users who shop online. Though existing anti-spam strategies have been proved to be effective in detecting traditional spam activities, evolving spam schemes can successfully cheat conventional testing by buying the comments of a massive number of random but genuine users which are sold by specific web markets, i.e., User Cloud. A more crucial problem is that such spam activities turn into a kind of 'advertising campaign' among business owners as they need to maintain their rank in the top few positions. In this paper, we propose a new Collaborative Marketing Hyping Detection solution, which aims to identify spam comments generated by the Spam Reviewer Cloud and to detect products which adopt an evolving spam strategy for promotion. Our experiments validate the existence of the Collaborative Marketing Hyping activities on a real-life e-commercial platform and also demonstrate that our model can effectively and accurately identify these advanced spam activities.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140102206
dc.relation.ispartof	IEEE Intelligent Systems	en_US
dc.relation.isbasedon	10.1109/MIS.2017.265120425	en_US
dc.relation.isreplacedby	10453/123917
dc.relation.isreplacedby	http://hdl.handle.net/10453/123917
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Collective Hyping Detection System for Identifying Online Spam Activities	en_US
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence And Image Processing	en_US
utslib.for	1702 Cognitive Science	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.declined	2017-10-02T19:18:11.412+1100
pubs.merge-to	10453/123917
pubs.merge-to	http://hdl.handle.net/10453/123917
pubs.deleted	2017-10-02T19:18:11.412+1100
pubs.publication-status	Accepted	en_US

Abstract:

IEEE Online reviews are extensively utilized by potential buyers to make business decisions. Unfortunately, fraudsters offer to write spam reviews for product promotion or competitor defamation, which drives online business holders to adopt this type of vicious strategy to increase their profits. These fake reviews always mislead users who shop online. Though existing anti-spam strategies have been proved to be effective in detecting traditional spam activities, evolving spam schemes can successfully cheat conventional testing by buying the comments of a massive number of random but genuine users which are sold by specific web markets, i.e., User Cloud. A more crucial problem is that such spam activities turn into a kind of 'advertising campaign' among business owners as they need to maintain their rank in the top few positions. In this paper, we propose a new Collaborative Marketing Hyping Detection solution, which aims to identify spam comments generated by the Spam Reviewer Cloud and to detect products which adopt an evolving spam strategy for promotion. Our experiments validate the existence of the Collaborative Marketing Hyping activities on a real-life e-commercial platform and also demonstrate that our model can effectively and accurately identify these advanced spam activities.

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

http://hdl.handle.net/10453/117999