Identifying Singleton Spammers via Spammer Group Detection

Kumar, Dhaneshwar; Shaalan, Yassien; Zhang, Xiuzhen; Chan, Jeffrey

doi:10.1007/978-3-319-93034-3_52

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…Apart from these, there exist specific models [55]- [60] as well which combine rating information with other behavioral footprints to detect spam reviewers [61]- [65]. Recently, many works have been reported in literature for detection of individual [66]- [68] as well as spammer groups using unsupervised learning [69]- [77], semi-supervised learning [56], [59], supervised learning [78]- [82] as well as hybrid learning [83]. Escalante et al [84] have emphasized on early detection of deception using profile based representations.…”

Section: Related Workmentioning

confidence: 99%

Detecting Product Review Spammers Using Principles of Big Data

Rout

Dalmia²,

Rath

et al. 2023

IEEE Trans. Eng. Manage.

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Growing consumerism has led to the importance of online reviews on the Internet. Opinions voiced by these reviews are taken into consideration by many consumers for making financial decisions online. This has led to the development of opinion spamming for profitable motives or otherwise. Work has been done to tackle the challenge of identifying such spammers, but the scale of the real-world review systems demands this problem to be tackled as a Big Data challenge. So, an effort has been made to detect online review spammers using the principle of Big Data. In this work, a rating-based model has been studied under the light of large-scale datasets (more than 80 million reviews by 20 million reviewers) using the Hadoop and Spark frameworks. Scale effects have been identified and mitigated to provide better context to large review systems. An improved computational framework has been presented to compute the overall spamcity of reviewers using exponential smoothing. The value of the smoothing factor was set empirically. Finally, future directions have been discussed.

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