Opinion spam detection: using multi-iterative graph-based model

The demand to detect opinionated spam, using opinion mining applications to prevent their damaging effects on e-commerce reputations is on the rise in many business sectors globally. The existing spam detection techniques in use nowadays, only consider one or two types of spam entities such as review, reviewer, group of reviewers, and product. Besides, they use a limited number of features related to behaviour, content and the relation of entities which reduces the detection's accuracy. Accordingly, these techniques mostly exploit synthetic datasets to analyse their model and are not able to be applied in the context of the real-world environment. As such, a novel graph-based model called “Multi-iterative Graph-based opinion Spam Detection” (MGSD) in which all various types of entities are considered simultaneously within a unified structure is proposed. Using this approach, the model reveals both implicit (i.e., similar entity's) and explicit (i.e., different entities’) relationships. The MGSD model is able to evaluate the ‘spamicity’ effects of entities more efficiently given it applies a novel multi-iterative algorithm which considers different sets of factors to update the spamicity score of entities. To enhance the accuracy of the MGSD detection model, a higher number of existing weighted features along with the novel proposed features from different categories were selected using a combination of feature fusion techniques and machine learning (ML) algorithms. The MGSD model can also be generalised and applied in various opinionated documents due to employing domain independent features. The output of the MGSD model showed that our feature selection and feature fusion techniques showed a remarkable improvement in detecting spam. The findings of this study showed that MGSD could improve the accuracy of state-of-the-art ML and graph-based techniques by around 5.6% and 4.8%, respectively, also achieving an accuracy of 93% for the detection of spam detection in our synthetic crowdsourced dataset and 95.3% for Ott's crowdsourced dataset

Automated Crowdturfing Attacks and Defenses in Online Review Systems

Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect. Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on "usefulness" metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers

개인 사회망 네트워크 분석 기반 온라인 사회 공격자 탐지

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 김종권.In the last decade we have witnessed the explosive growth of online social networking services (SNSs) such as Facebook, Twitter, Weibo and LinkedIn. While SNSs provide diverse benefits – for example, fostering inter-personal relationships, community formations and news propagation, they also attracted uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and widely. Spams, unsolicited or inappropriate messages, significantly impair the credibility and reliability of services. Therefore, detecting spammers has become an urgent and critical issue in SNSs. This paper deals with spamming in Twitter and Weibo. Instead of spreading annoying messages to the public, a spammer follows (subscribes to) normal users, and followed a normal user. Sometimes a spammer makes link farm to increase target accounts explicit influence. Based on the assumption that the online relationships of spammers are different from those of normal users, I proposed classification schemes that detect online social attackers including spammers. I firstly focused on ego-network social relations and devised two features, structural features based on Triad Significance Profile (TSP) and relational semantic features based on hierarchical homophily in an ego-network. Experiments on real Twitter and Weibo datasets demonstrated that the proposed approach is very practical. The proposed features are scalable because instead of analyzing the whole network, they inspect user-centered ego-networks. My performance study showed that proposed methods yield significantly better performance than prior scheme in terms of true positives and false positives.최근 우리는 Facebook, Twitter, Weibo, LinkedIn 등의 다양한 사회 관계망 서비스가 폭발적으로 성장하는 현상을 목격하였다. 하지만 사회 관계망 서비스가 개인과 개인간의 관계 및 커뮤니티 형성과 뉴스 전파 등의 여러 이점을 제공해 주고 있는데 반해 반갑지 않은 현상 역시 발생하고 있다. 스패머들은 사회 관계망 서비스를 동력 삼아 스팸을 매우 빠르고 넓게 전파하는 식으로 악용하고 있다. 스팸은 수신자가 원치 않는 메시지들을 일컽는데 이는 서비스의 신뢰도와 안정성을 크게 손상시킨다. 따라서, 스패머를 탐지하는 것이 현재 소셜 미디어에서 매우 긴급하고 중요한 문제가 되었다. 이 논문은 대표적인 사회 관계망 서비스들 중 Twitter와 Weibo에서 발생하는 스패밍을 다루고 있다. 이러한 유형의 스패밍들은 불특정 다수에게 메시지를 전파하는 대신에, 많은 일반 사용자들을 '팔로우(구독)'하고 이들로부터 '맞 팔로잉(맞 구독)'을 이끌어 내는 것을 목적으로 하기도 한다. 때로는 link farm을 이용해 특정 계정의 팔로워 수를 높이고 명시적 영향력을 증가시키기도 한다. 스패머의 온라인 관계망이 일반 사용자의 온라인 사회망과 다를 것이라는 가정 하에, 나는 스패머들을 포함한 일반적인 온라인 사회망 공격자들을 탐지하는 분류 방법을 제시한다. 나는 먼저 개인 사회망 내 사회 관계에 주목하고 두 가지 종류의 분류 특성을 제안하였다. 이들은 개인 사회망의 Triad Significance Profile (TSP)에 기반한 구조적 특성과 Hierarchical homophily에 기반한 관계 의미적 특성이다. 실제 Twitter와 Weibo 데이터셋에 대한 실험 결과는 제안한 방법이 매우 실용적이라는 것을 보여준다. 제안한 특성들은 전체 네트워크를 분석하지 않아도 개인 사회망만 분석하면 되기 때문에 scalable하게 측정될 수 있다. 나의 성능 분석 결과는 제안한 기법이 기존 방법에 비해 true positive와 false positive 측면에서 우수하다는 것을 보여준다.1 Introduction 1 2 Related Work 6 2.1 OSN Spammer Detection Approaches 6 2.1.1 Contents-based Approach 6 2.1.2 Social Network-based Approach 7 2.1.3 Subnetwork-based Approach 8 2.1.4 Behavior-based Approach 9 2.2 Link Spam Detection 10 2.3 Data mining schemes for Spammer Detection 10 2.4 Sybil Detection 12 3 Triad Significance Profile Analysis 14 3.1 Motivation 14 3.2 Twitter Dataset 18 3.3 Indegree and Outdegree of Dataset 20 3.4 Twitter spammer Detection with TSP 22 3.5 TSP-Filtering 27 3.6 Performance Evaluation of TSP-Filtering 29 4 Hierarchical Homophily Analysis 33 4.1 Motivation 33 4.2 Hierarchical Homophily in OSN 37 4.2.1 Basic Analysis of Datasets 39 4.2.2 Status gap distribution and Assortativity 44 4.2.3 Hierarchical gap distribution 49 4.3 Performance Evaluation of HH-Filtering 53 5 Overall Performance Evaluation 58 6 Conclusion 63 Bibliography 65Docto

Dissecting AI-Generated Fake Reviews: Detection and Analysis of GPT-Based Restaurant Reviews on Social Media

Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, posing challenges for social media platforms to detect this kind of content. This study addresses two research questions: (1) the effective detection of AI-generated restaurant reviews generated from high-quality elite authentic reviews, and (2) the comparison of out-of-sample predicted AI-generated reviews and authentic reviews across multiple dimensions of review, user, restaurant, and content characteristics. We fine-tuned a GPT text detector to predict fake reviews, significantly outperforming existing solutions. We applied the model to predict non-elite reviews that already passed the Yelp filtering system, revealing that AI-generated reviews typically score higher ratings, users posting such content have less established Yelp reputations and AI-generated reviews are more comprehensible and less linguistically complex than human-generated reviews. Notably, machine-generated reviews are more prevalent in low-traffic restaurants in terms of customer visits

Artificial and Natural Topic Detection in Online Social Networks

Online Social Networks (OSNs), such as Twitter, offer attractive means of social interactions and communications, but also raise privacy and security issues. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside a huge volume of data from several themes, topics, and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for TopicModeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph community architecture (density and degree) allows the indication of a topic strength and the classification of it as natural or artificial. The later created by the spammers on OSNs

Fake review detection using time series

Today’s e-commerce is highly depended on online customers’ reviews posted in opinion sharing websites that are growing incredibly. These reviews are important not only effect on potential customers’ purchase decision but also for manufacturers and business holders to reshape and customize their products and manage competition with rivals throughout the market place. Moreover opinion mining techniques that analyze customer reviews obtained from opinion sharing websites for different purposes could not reveal accurate results for combination of spam reviews and truthful reviews in datasets. Thus employing review spam detection techniques in review websites are highly essential in order to provide reliable resources for customers, manufacturers and researchers. This study aims to detect spam reviews using time series. To achieve this, the novel proposed method detects suspicious time intervals with high number of reviews. Then a combination of three features, i.e. rating of reviews, similarity percentage of review contexts and number of other reviews written by the reviewer of current review, will be used to score each review. Finally a threshold defined for total scores assigned to reviews will be the border line between spam and genuine reviews. Evaluation of obtained results reveals that the proposed method is highly effective in distinguishing spam and non-spam reviews. Furthermore combination of all features used in this research exposed the best results. This fact represents the effectiveness of each feature

From Understanding Telephone Scams to Implementing Authenticated Caller ID Transmission

abstract: The telephone network is used by almost every person in the modern world. With the rise of Internet access to the PSTN, the telephone network today is rife with telephone spam and scams. Spam calls are significant annoyances for telephone users, unlike email spam, spam calls demand immediate attention. They are not only significant annoyances but also result in significant financial losses in the economy. According to complaint data from the FTC, complaints on illegal calls have made record numbers in recent years. Americans lose billions to fraud due to malicious telephone communication, despite various efforts to subdue telephone spam, scam, and robocalls. In this dissertation, a study of what causes the users to fall victim to telephone scams is presented, and it demonstrates that impersonation is at the heart of the problem. Most solutions today primarily rely on gathering offending caller IDs, however, they do not work effectively when the caller ID has been spoofed. Due to a lack of authentication in the PSTN caller ID transmission scheme, fraudsters can manipulate the caller ID to impersonate a trusted entity and further a variety of scams. To provide a solution to this fundamental problem, a novel architecture and method to authenticate the transmission of the caller ID is proposed. The solution enables the possibility of a security indicator which can provide an early warning to help users stay vigilant against telephone impersonation scams, as well as provide a foundation for existing and future defenses to stop unwanted telephone communication based on the caller ID information.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Detecting Abnormal Behavior in Web Applications

The rapid advance of web technologies has made the Web an essential part of our daily lives. However, network attacks have exploited vulnerabilities of web applications, and caused substantial damages to Internet users. Detecting network attacks is the first and important step in network security. A major branch in this area is anomaly detection. This dissertation concentrates on detecting abnormal behaviors in web applications by employing the following methodology. For a web application, we conduct a set of measurements to reveal the existence of abnormal behaviors in it. We observe the differences between normal and abnormal behaviors. By applying a variety of methods in information extraction, such as heuristics algorithms, machine learning, and information theory, we extract features useful for building a classification system to detect abnormal behaviors.;In particular, we have studied four detection problems in web security. The first is detecting unauthorized hotlinking behavior that plagues hosting servers on the Internet. We analyze a group of common hotlinking attacks and web resources targeted by them. Then we present an anti-hotlinking framework for protecting materials on hosting servers. The second problem is detecting aggressive behavior of automation on Twitter. Our work determines whether a Twitter user is human, bot or cyborg based on the degree of automation. We observe the differences among the three categories in terms of tweeting behavior, tweet content, and account properties. We propose a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Furthermore, we shift the detection perspective from automation to spam, and introduce the third problem, namely detecting social spam campaigns on Twitter. Evolved from individual spammers, spam campaigns manipulate and coordinate multiple accounts to spread spam on Twitter, and display some collective characteristics. We design an automatic classification system based on machine learning, and apply multiple features to classifying spam campaigns. Complementary to conventional spam detection methods, our work brings efficiency and robustness. Finally, we extend our detection research into the blogosphere to capture blog bots. In this problem, detecting the human presence is an effective defense against the automatic posting ability of blog bots. We introduce behavioral biometrics, mainly mouse and keyboard dynamics, to distinguish between human and bot. By passively monitoring user browsing activities, this detection method does not require any direct user participation, and improves the user experience