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 Recently, fake news detection on social media (SM) has attracted a lot of attention. With the emergence of fake news at a breakneck pace, the massive spread of fake news has had a serious impact in our society. The authenticity of the news is questionable and there exists a necessity for an automated tool for the detection. However, most fake news detection methods are mainly supervised, requiring huge amounts of annotated data, which is time-consuming, expensive, and almost impossible with vast new SM volume. To deal with this problem, in this paper, we propose a novel unsupervised fake news detection framework based on structural contrastive learning by combining the propagation structure of news and contrastive learning to achieve unsupervised training. To validate the influence of parameters and our method’s performance, we design experiment sets on public Twitter and Weibo datasets, which validate our approach outperforms current baseline ones and has proper robustness.

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.

Abstract It is not uncommon for malicious sellers to collude with fake reviewers (also called spammers) to write fake reviews for multiple products to either demote competitors or promote their products’ reputations, forming a gray industry chain. To detect spammer groups in a heterogeneous network with rich semantic information from both buyers and sellers, researchers have conducted extensive research using Frequent Item Mining-based and graph-based methods. However, these methods cannot detect spammer groups with cross-product attacks and do not jointly consider structural and attribute features, and structure-attribute correlation, resulting in poorer detection performance. Therefore, we propose a collaborative training-based spammer group detection algorithm by constructing a heterogeneous induced sub-network based on the target product set to detect cross-product attack spammer groups. To jointly consider all available features, we use the collaborative training method to learn the feature representations of nodes. In addition, we use the DBSCAN clustering method to generate candidate groups, exclude innocent ones, and rank them to obtain spammer groups. The experimental results on real-world datasets indicate that the overall detection performance of the proposed method is better than that of the baseline methods.

Due to the flexibility of heterogeneous information networks (HINs) in modeling heterogeneous data, researchers begin to use it to integrate the objects and relationships in recommender systems. However, how to abstract and exploit effective information and apply the information to recommender systems is a challenge. To fully mine nodes' structural features and better integrate these features simultaneously, we present a nonlinear feature fusion-based rating prediction algorithm. This algorithm first uses a meta-path-based HIN embedding model to extract the nodes' structural features. Then, the structural features are converted by a nonlinear fusion method. Finally, the fused features are input into the multilayer perceptron to achieve rating prediction. Experiments on real-life data sets, such as Movielens-100k, Yelp, Douban Book, and Douban Movie, are designed to prove the performance of the proposed model. Experimental results on the four data sets reveal that our algorithm is superior to the baselines.

As a promising paradigm, VANET has been attracting more and more attention from the industry and academia recently. However, due to rapid movement of vehicles and highly dynamic topology, designing efficient MAC protocol for VANET is still challenging. In this paper, we propose MAC-AC, a novel TDMA-based distributed MAC protocol designed specifically for a vehicular ad hoc network. In MAC-AC, when multiple vehicles compete for the same time slot within their two-hop communication range, one of contending vehicle can obtain this time slot by a simple method, which increases the success probability of vehicles accessing channel. Analysis results are presented to demonstrate the efficiency of MAC-AC and compare it to ADHOC MAC, an existing MAC protocol based on TDMA.

Abstract Microblog is a popular social network in which hot topics propagate online rapidly. Real-time topic detection can not only understand public opinion well but also bring high commercial value. We design a method for real-time microblog data analysis in order to detect popular long lasting events as well as emerging events. Firstly, a mining frequent items algorithm on microblog data stream is proposed to count approximate word frequency. This mining frequent items algorithm can find the frequent words for some time. Secondly, the windows size of the monitored words is adjusted dynamically according to the duration time and the evolution of events. Lastly, new topics and trends of existing topics can be detected by using dynamic clustering algorithm based on vector space model. Experimental results show that the proposed algorithms can improve performance in terms of running time and accuracy.

The article A whitelist and blacklist-based co-evolutionary strategy for defensing against multifarious trust attacks, written by Shujuan Ji, Haiyan Ma, Yongquan Liang, Hofung Leung and Chunjin Zhang, was originally published electronically on the publisher’s internet portal.

With electronic commerce becoming increasingly popular, the problems of trust have become one of the main challenges in the development of electronic commerce. Although various mechanisms have been adopted to guarantee trust between customers and sellers (or platforms), trust and reputation systems are still frequently attacked by deceptive, collusive, or strategic agents. Therefore, it is difficult to keep these systems robust. It has been mentioned that a combined usage of both trust and distrust propagation can lead to better results. However, little work has been known to realize this insight successfully. Besides, literatures either use a social network with trust/distrust information or use one advisor list in evaluating all sellers, which leads to the lack of pertinence and inaccuracy of evaluation. This paper proposes a defensing strategy called WBCEA , in which, each buyer agent is modeled with two attributes (i.e., the trustworthy facet and the untrustworthy facet) and two lists (i.e., the whitelist and the blacklist). Based on the social network that are constructed and maintained according to its whitelist and blacklist, the honest buyer agent can find trustable buyers and evaluate the candidate sellers according to its own experience and ratings of trustable buyers. Experiments are designed and implemented to verify the accuracy and robustness of this strategy. Results show that our strategy outperforms existing ones, especially when majority of buyers are dishonest in the electronic market.

This paper presents an adaptive prediction-regret driven negotiation strategy for bilateral bargaining without modeling opponents, which combines the prediction idea in heuristic method and the regret principle in psychology. Experimental results show that agents that employ this strategy outperform agents that use other strategies previously proposed in the literature.