Detecting review spammer groups in dynamic review networks

Hu, Mengxiao; Xu, Guangxia; Ma, Chuang; Daneshmand, Mahmoud

doi:10.1145/3321408.3323077

Cited by 4 publications

(9 citation statements)

References 18 publications

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“…The model marked top-ranked groups as spammer groups. In a similar context, Hu et al (2019) used the CPM method to find spammer groups with the infinite change in the review stream.…”

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). This research aims to develop a framework that will use both behavioral and graph features.…”

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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“…The model marked top-ranked groups as spammer groups. In a similar context, Hu et al (2019) used the CPM method to find spammer groups with the infinite change in the review stream.…”

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

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“…Given the increasing activities of spammer groups, numerous researchers have been motivated to develop algorithms to detect these groups to mitigate their detrimental impact (Mukherjee et al 2012;Xu et al 2013;Xu and Zhang 2015;Wang et al 2016Wang et al , 2018aChoo et al 2015;Hu et al 2019;Zhang et al 2020Zhang et al , 2022aZhang et al , b, 2023Akoglu et al 2013;Ye and Akoglu 2015;Zheng et al 2018;Zhu et al 2019;Shehnepoor et al 2021;Chao et al 2022;Li et al 2017;Ji et al 2020;Liu et al 2018). According to the approach of generating candidate groups, existing spammer group detection algorithms can be classified into Frequent Item Mining (FIM)-based, graph-based, and burst-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The FIM-based algorithms (Mukherjee et al 2012;Xu et al 2013;Xu and Zhang 2015) aim to capture repetitive fraudulent actions of reviewers to identify spammer groups. The graph-based algorithms (Wang et al 2016(Wang et al , 2018aChoo et al 2015;Hu et al 2019;Zhang et al 2020Zhang et al , 2022aAkoglu et al 2013;Ye and Akoglu 2015;Zheng et al 2018;Zhu et al 2019;Shehnepoor et al 2021;Chao et al 2022) leverage the network relationships of reviewers to construct either homogeneous or heterogeneous graphs and then, based on the relational or behavioral information of the graph, generate candidate groups through clustering or community detection techniques. The burst-based algorithms (Li et al 2017;Ji et al 2020;Liu et al 2018) capture the burst intervals of reviews from either reviewers or products, using these intervals to determine candidate groups.…”

Section: Introductionmentioning

confidence: 99%

“…(1) Proposes a novel optimization algorithm for coreviewing graphs. Existing graph-based algorithms predominantly focus on utilizing the similarity of co-reviewed products among reviewers as the edge weights in the co-reviewing graph to adapt its structure (Wang et al 2018a;Hu et al 2019;Zhang et al 2020). Different from these literatures, our algorithm uses a behavioral consistency index that combines both product consistency and rating consistency in their scores to remove unnecessary coreviewing connections within the graph.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing fairness of trading environment: discovering overlapping spammer groups with dynamic co-review graph optimization

Wang,

Li,

et al. 2024

Cybersecurity

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Within the thriving e-commerce landscape, some unscrupulous merchants hire spammer groups to post misleading reviews or ratings, aiming to manipulate public perception and disrupt fair market competition. This phenomenon has prompted a heightened research focus on spammer groups detection. In the e-commerce domain, current spammer group detection algorithms can be classified into three categories, i.e., Frequent Item Mining-based, graph-based, and burst-based algorithms. However, existing graph-based algorithms have limitations in that they did not adequately consider the redundant relationships within co-review graphs and neglected to detect overlapping members within spammer groups. To address these issues, we introduce an overlapping spammer group detection algorithm based on deep reinforcement learning named DRL-OSG. First, the algorithm filters out highly suspicious products and gets the set of reviewers who have reviewed these products. Secondly, taking these reviewers as nodes and their co-reviewing relationships as edges, we construct a homogeneous co-reviewing graph. Thirdly, to efficiently identify and handle the redundant relationships that are accidentally formed between ordinary users and spammer group members, we propose the Auto-Sim algorithm, which is a specifically tailored algorithm for dynamic optimization of the co-reviewing graph, allowing for adjustments to the reviewers’ relationship network within the graph. Finally, candidate spammer groups are discovered by using the Ego-Splitting overlapping clustering algorithm, allowing overlapping members to exist in these groups. Then, these groups are refined and ranked to derive the final list of spammer groups. Experimental results based on real-life datasets show that our proposed DRL-OSG algorithm performs better than the baseline algorithms in Precision.

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Two-stage anomaly detection algorithm via dynamic community evolution in temporal graph

Jiang

Liu

2022

Appl Intell

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Detecting review spammer groups in dynamic review networks

Cited by 4 publications

References 18 publications

Spammer group detection and diversification of customers’ reviews

Spammer group detection and diversification of customers’ reviews

Enhancing fairness of trading environment: discovering overlapping spammer groups with dynamic co-review graph optimization

Two-stage anomaly detection algorithm via dynamic community evolution in temporal graph

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