Reputation fraud campaigns (RFCs) distort the reputations of rated items, by generating fake ratings through multiple spammers. One effective way of detecting RFCs is to characterize their collective behaviors based on rating histories.However, these campaigns are constantly evolving and changing tactics to evade detection.For example, they can launch early attacks on the items to quickly dominate the reputations.They can also whitewash themselves through creating new accounts for subsequent attacks.It is thus challenging for existing approaches working on historical data to promptly react to such emerging fraud activities.In this paper, we conduct RFC detection in online fashion, so as to spot campaign activities as early as possible.This leads to a unified and scalable optimization framework, FraudScan, that can adapt to emerging fraud patterns over time.Empirical analysis on two real-world datasets validates the effectiveness and efficiency of the proposed framework.

We address the collusive opinion fraud problem in online review portals, where groups of people work together to deliver deceptive reviews for manipulating the reputations of targeted items. Such collusive fraud is considered much harder to defend against, since the participants (or colluders) can evade detection by shaping their behaviors collectively so as not to appear suspicious. To alleviate this problem, countermeasures have been proposed that leverage the collective behaviors of colluders. The motivation stems from the observation that colluders typically act in a very synchronized way, as they are instructed by the same campaigns with common items to target and schedules to follow. However, the collective behaviors examined in existing solutions focus mostly on the external appearance of fraud campaigns, such as the campaign size and the size of the targeted item set. These signals may become ineffective once colluders have changed their behaviors collectively. Moreover, the detection algorithms used in existing approaches are designed to only make collusion inference on the input data; predictive models that can be deployed for detecting emerging fraud cannot be learned from the data. In this article, to complement existing studies on collusive opinion fraud characterization and detection, we explore more subtle behavioral trails in collusive fraud practice. In particular, a suite of homogeneity-based measures are proposed to capture the interrelationships among colluders within campaigns. Moreover, a novel statistical model is proposed to further characterize, recognize, and predict collusive fraud in online reviews. The proposed model is fully unsupervised and highly flexible to incorporate effective measures available for better modeling and prediction. Through experiments on two real-world datasets, we show that our method outperforms the state of the art in both characterization and detection abilities.

Online review fraud has evolved in sophistication by launching intelligent campaigns where a group of coordinated participants work together to deliver deceptive reviews for the designated targets. Such collusive fraud is considered much harder to defend against as these campaign participants are capable of evading detection by shaping their behaviors collectively so as not to appear suspicious. The present work complements existing studies by exploring more subtle behavioral trails connected with collusive review fraud. A novel statistical model is proposed to further characterize, recognize, and forecast collusive fraud in online reviews. The proposed model is completely unsupervised, which bypasses the difficulty of manual annotation required for supervised modeling. It is also highly flexible to incorporate collusion characteristics available for better modeling and prediction. Experiments on two real-world datasets demonstrate the effectiveness of the proposed method and the improvements in learning and predictive abilities.

Spam campaigns spotted in popular product review websites (e.g., amazon.com) have attracted mounting attention from both industry and academia, where a group of online posters are hired to collaboratively craft deceptive reviews for some target products. The goal is to manipulate perceived reputations of the targets for their best interests. Many efforts have been made to detect such colluders by extracting pointwise features from individual reviewers/reviewer-groups, however, pairwise features which can potentially capture the underlying correlations among colluders are either ignored or just explored insufficiently in the literature. We observed that pairwise features can be more robust to model the relationships among colluders since they, as the ingredients of spam campaigns, are correlated in nature. In this paper, we explore multiple heterogeneous pairwise features in virtue of some collusion signals found in reviewers' rating behaviors and linguistic patterns. In addition, an unsupervised and intuitive colluder detecting framework has been proposed which can benefit from these pairwise features. Extensive experiments on real dataset show the effectiveness of our method and satisfactory superiority over several competitors.

Location-based messaging applications (LMAs), a kind of messaging applications for mobile devices which enable users to connect with people based on their geographical locations, have recently experienced a huge popularity growth. The killer feature in LMAs that embodies the concept of geo-based instant messaging, named people nearby, allows users at any place to search and communicate with other registered users nearby. In this paper, we discuss a common weakness in LMAs that relates to the abuse of the people nearby function. In this case, rich personal data of registered LMA users can be easily obtained, bringing a chance to perform automated user profiling in LMAs. Specifically, we build an automated and scalable system to construct extended profiles (or we call life profile) of LMA users, which contain not only personal information of LMA users but also the daily activities and social ties inferred from their leaked spatio-temporal privacy. The system is highly adaptable to various applications, requiring no modification of applications or trivial work on protocol reverse engineering. We conduct the evaluation on a large scale for the first time. In our experiment, we succeed to construct life profiles for more than 280,000 users from two popular LMAs. The results of empirical analysis not only validate the existence of the privacy issue in LMAs, but also demonstrate its severity.

As the rapid development of China's e-commerce in recent years and the underlying evolution of adversarial spamming tactics, more sophisticated spamming activities may carry out in Chinese review websites. Empirical analysis, on recently crawled product reviews from a popular Chinese e-commerce website, reveals the failure of many state-of-the-art spam indicators on detecting collusive spammers. Two novel methods are then proposed: 1) a KNN-based method that considers the pairwise similarity of two reviewers based on their group-level relational information and selects k most similar reviewers for voting; 2) a more general graph-based classification method that jointly classifies a set of reviewers based on their pairwise transaction correlations. Experimental results show that both our methods promisingly outperform the indicator-only classifiers in various settings.