Collective Hyping Detection System for Identifying Online Spam Activities
- Publication Type:
- Journal Article
- Citation:
- IEEE Intelligent Systems, 2017, 32 (5), pp. 53 - 63
- Issue Date:
- 2017-09-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 08070899.pdf | Published Version | 6.88 MB | |||
| x5zhang.final.Intelligent-Systems.pdf | Accepted Manuscript | 1.35 MB |
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© 2001-2011 IEEE. Although existing antispam strategies detect traditional spam activities effectively, evolving spam schemes can successfully cheat conventional testing by buying the comments that are written by genuine users and sold by specific web markets. Such spam activities turn into a kind of advertising campaign among business owners to maintain their rank in top positions. This article proposes a new collaborative marketing hyping detection solution that aims to identify spam comments generated by the Spam Reviewer Cloud and detect products that adopt an evolving spam strategy for promotion. The authors propose an unsupervised learning model that combines heterogeneous product review networks in an attempt to discover collective hyping activities. Their experiments validate the existence of the collaborative marketing hyping activities on a real-life ecommerce platform and demonstrate that their model can effectively and accurately identify these advanced spam activities.
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