Collusion Detection in Online Rating Systems

Allahbakhsh, Mohammad; Ignjatović, Aleksandar; Benatallah, Boualem; Beheshti, Amin; Bertino, Elisa; Foo, Norman Y.

doi:10.1007/978-3-642-37401-2_21

Cited by 66 publications

(36 citation statements)

References 11 publications

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“…They used the frequent itemset mining method to get candidate groups and proposed GSRank framework for identifying the spam groups. Allahbakhsh et al (2013) also used frequent item mining techniques. Spammer behavioral features like review time and rating scores were used to detect group spammers.…”

Section: Spammer Group Detection Methodsmentioning

confidence: 99%

“…Considering the existing work on spam group detection, most of the related studies (Mukherjee, Liu & Glance, 2012;Allahbakhsh et al, 2013;Zhang, Wu & Cao, 2017;Zhou, Liu & Zhang, 2018) have used spammer behavioral features to detect spam groups. On the other hand, some researchers used graph-based techniques to identify suspicious spammer groups with a little focus on spammer behavioral features (Rayana & Akoglu, 2015;Li et al, 2017;Kaghazgaran, Caverlee & Squicciarini, 2018;Zhang et al, 2018;Xu & Zhang, 2016;Xu et al, 2019;Hu et al, 2019).…”

Section: Spammer Group Detection Methodsmentioning

confidence: 99%

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Spammer group detection and diversification of customers’ reviews

Hussain

Mirza

Ali

et al. 2021

PeerJ Computer Science

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Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profit or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, Spam Review Detection problem (SRD) has gained much attention from researchers, but still there is a need to identify review spammers who often work collaboratively to promote or demote targeted products. It can severely harm the review system. This work presents the Spammer Group Detection (SGD) method which identifies suspicious spammer groups based on the similarity of all reviewer’s activities considering their review time and review ratings. After removing these identified spammer groups and spam reviews, the resulting non-spam reviews are displayed using diversification technique. For the diversification, this study proposed Diversified Set of Reviews (DSR) method which selects diversified set of top-k reviews having positive, negative, and neutral reviews/feedback covering all possible product features. Experimental evaluations are conducted on Roman Urdu and English real-world review datasets. The results show that the proposed methods outperformed the existing approaches when compared in terms of accuracy.

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Section: Spammer Group Detection Methodsmentioning

confidence: 99%

Section: Spammer Group Detection Methodsmentioning

confidence: 99%

Spammer group detection and diversification of customers’ reviews

Hussain

Mirza

Ali

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Collusive behaviour is a prevalent issue in systems where reputation can be accrued [1,35]. In general, such behaviour is characterised by two or more users cooperatively boosting their reputation by providing multiple high ratings to each other, while ignoring or giving low ratings to other users.…”

Section: Potential Collusive Behaviourmentioning

confidence: 99%

“…(2) Manipulative activity. It is also to be expected, given the potential benefits of having a high reputation on commonfare.net, that users may try to increase their perceived contribution by creating meaningless content, or colluding with other users by repeatedly engaging in meaningless interactions [1,35]. Ensuring these users are not rewarded for such actions is necessary to keep commonfare.net fair and equal.…”

Section: Network Analysis Viewmentioning

confidence: 99%

Supporting Social Innovation through Visualisations of Community Interactions

Rough

Paoli

Botto

2019

Proceedings of the 9th International Conference on Communities &Amp; Technologies - Transforming Communities

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Online communities that form through the introduction of sociotechnical platforms require significant effort to cultivate and sustain. Providing open, transparent information on community behaviour can motivate participation from community members themselves, while also providing platform administrators with detailed interaction dynamics. However, challenges arise in both understanding what information is conducive to engagement and sustainability, and then how best to represent this information to platform stakeholders. Towards a better understanding of these challenges, we present the design, implementation, and evaluation of a set of simple visualisations integrated into a Collective Awareness Platform for Social Innovation platform titled commonfare.net. We discuss the promise and challenge of bringing social innovation into the digital age, in terms of supporting sustained platform use and collective action, and how the introduction of community visualisations has been directed towards achieving this goal.

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“…What makes this app suspicious (we will later confirm that this app is abused) are: 1) there is a huge spike of number of 5-star ratings in week 2; 2) The spike only occurred on 5-star rating. Mohammad, et al [8] proposes the idea of collusion group in rating manipulation. For app's rating manipulation, [6] modifies the definition of collusion group and gives the following definition: Definition 6.…”

Section: Definitionmentioning

confidence: 99%

A Machine Learning Based Approach for Mobile App Rating Manipulation Detection

Song¹,

Chen²,

Zhu³

et al. 2019

ICST Transactions on Security and Safety

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In order to promote apps in mobile app stores, for malicious developers and users, manipulating average rating is a popular and feasible way. In this work, we propose a two-phase machine learning approach to detecting app rating manipulation attacks. In the first learning phase, we generate feature ranks for different app stores and find that top features match the characteristics of abused apps and malicious users. In the second learning phase, we choose top N features and train our models for each app store. With cross-validation, our training models achieve 85% f-score. We also use our training models to discover new suspicious apps from our data set and evaluate them with two criteria. Finally, we conduct some analysis based on the suspicious apps classified by our training models and some interesting results are discovered.

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Collusion Detection in Online Rating Systems

Cited by 66 publications

References 11 publications

Spammer group detection and diversification of customers’ reviews

Spammer group detection and diversification of customers’ reviews

Supporting Social Innovation through Visualisations of Community Interactions

A Machine Learning Based Approach for Mobile App Rating Manipulation Detection

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