Online reviews on platforms such as Amazon and Yelp significantly influence consumer decisions and business reputations. However, spammers form groups that strategically control product reviews over specific periods to manipulate consumer sentiment and decision-making. Traditional spammer group detection methods face two primary issues: 1) knowledge marginalization: interactions dominate the model to form candidate groups, potentially marginalizing valuable structured knowledge; and 2) temporal knowledge discrepancy: inconsistencies in users’ temporal activities and behavioral features lead to blurry classification of candidate groups. To address these two limitations, we introducetemporal knowledge consistency for spammer groups detection via contrastive learningcalled TKCCL. We employ dual-view encoders derived from knowledge graphs and heterogeneous information networks to learn informative representations, thereby alleviating knowledge marginalization. TKCCL maps the temporal knowledge as vectors to measure consistency, enhancing users’ rating proximity and temporal synchronization in dual views, thereby reducing temporal knowledge discrepancies. We optimize spammer group detection by modeling it as a greedy set cover problem, which enhances the method’s responsiveness to dynamic spam strategies. Experimental results on four public datasets demonstrate that TKCCL substantially outperforms existing methods. Our code is available athttps://github.com/NeenLee/TKCCL.

Nowadays, the widespread of false information has brought great harm to society, thus the demand for Multimodal Fake News Detection (MFND) is becoming increasingly urgent. Currently, traditional isolated trained detectors face challenges in directly acquiring open-world facts. The advent of Multi-modal Large Language Models (MLLMs) offers one potential solution to this challenge. In this article, we first investigate the potential of MLLMs in MFND and find that: (1) their accuracy in detecting Fake News is significantly lower than traditional detectors; (2) although MLLMs can generate reasoning grounds that are highly related to human cognition, there are still some problems in their analysis process, such as missing key information and logical faults. Based on these findings, we propose that current MLLMs cannot directly replace conventional detectors but can provide them with evidence and knowledge from multiple perspectives. Based on this proposal, we design an MLLMs-guided Multi-source Information Fusion Network (MMFN) for MFND. In MLLMs, a layer-by-layer human cognitive path is simulated to provide reasoning analysis and relevant background knowledge for MFND. Simultaneously, a Fine-grained Clues Extraction (FCE) module that combines attention and uncertainty reasoning is designed to capture both similar clues and ambiguous clues. Finally, an Uncertainty-driven Adaptive Fusion Network (UAFN) is designed to adaptively mine key information and perform weighting of information at different levels. The experimental results verified on four popular fake news datasets demonstrate the superiority of our method.

Abstract Malicious sellers frequently collaborate with spammers to fabricate reviews for promoting their products. These spammers act strategically in groups and have even formed black-and-gray industry chains. Researchers have proposed Frequent Item Mining-based, review burst-based, and graph-based schemes for spammer group detection. However, existing graph-based schemes often model reviewer relationships as a homogeneous network, failing to fully utilize the relationship semantics between reviewers or account for the burst characteristics of reviews, resulting in poor detection performance. Thus, this research proposes a cross-view contrastive learning-based spammer group detection algorithm for heterogeneous networks. To mine and embed the burst characteristics of reviews, we first filter out the target products and mine the active sessions of reviews about these products. We then construct a heterogeneous network featuring four node types and devise three unique meta-paths. Additionally, we utilize the cross-view contrastive learning method to learn the reviewer embeddings and apply DBSCAN to identify suspected groups for subsequent cleansing and ranking, ultimately determining potential spammer groups. Our experiments show the detection performance of the proposed scheme outperforms baseline ones.

Abstract 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.