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

Survey on Ranking Fraud for Mobile Apps

In today's world there are many fraud ways through which app developers try to put their app at the first position. The developers try hard to configure the positions of various apps in the list of apps in that particular area. Mobile phones operating system is developing day by day but research in fraud apps is limited or not much discovered. Fraud ranking in mobile phones lead to download of the false app which allows damaging the mobile phones and falsely getting famous by that false apps. Fraud ranking in mobile phones is very important and this paper shows the misinterpretation of the apps information and configured apps position. Also a framework is used for fraud detection in apps. The work is grouped basically into three categories. First is web ranking spam detection, second is the online review spam detection and third one is mobile app recommendation. The first method Web ranking spam refers to any kind of actions which bring to selected Web pages an unjustifiable favorable relevance or give much importance. The second one is Review spam which is designed to give unfair view of some objects so as to influence the consumers' perception of the objects by directly or indirectly damaging the object's reputation. The third one is mobile app recommendation which tells users to check the app usage record

Discovery of Ranking Fraud for Mobile Apps

Ranking fraud within the mobile App market refers to deceitful or deceptive activities that have a purpose of bumping up the Apps within the quality list. Indeed, it becomes a lot of and a lot of frequent for App developers to use shady means that, like inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. Whereas the importance of preventing ranking fraud has been well known, there's restricted understanding and analysis during this space. to the current finish, during this paper, we offer a holistic read of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we have a tendency to initial propose to accurately find the ranking fraud by mining the active periods, specifically leading sessions, of mobile Apps. Such leading sessions are often leveraged for sleuthing the native anomaly rather than international anomaly of App rankings. what is more, we have a tendency to investigate 3 kinds of evidences, i.e., ranking primarily based evidences, rating based evidences and review based evidences, by modeling Apps’ ranking, rating and review behaviors through applied math hypotheses tests

Towards eradication of SPAM: A study on intelligent adaptive SPAM filters

As the massive increase of electronic mail (email) usage continues, SPAM (unsolicited bulk email), has continued to grow because it is a very inexpensive method of advertising. These unwanted emails can cause a serious problem by filling up the email inbox and thereby leaving no space for legitimate emails to pass through. Currently the only defense against SPAM is the use of SPAM filters. A novel SPAM filter GetEmail5 along with the design rationale, is described in this thesis. To test the efficacy of GetEmail5 SPAM filter, an experimental setup was created and a commercial bulk email program was used to send SPAM and non-SPAM emails to test the new SPAM filter. GetEmail5's efficiency and ability to detect SPAM was compared against two highly ranked commercial SPAM filters on different sets of emails, these included all SPAM, non-SPAM, and mixed emails, also text and HTML emails. The results showed the superiority of GetEmail5 compared to the two commercial SPAM filters in detecting SPAM emails and reducing the user's involvement in categorizing the incoming emails. This thesis demonstrates the design rationale for GetEmail5 and also its greater effectiveness in comparison with the commercial SPAM filters tested

Camouflages and Token Manipulations-The Changing Faces of the Nigerian Fraudulent 419 Spammers

The inefficiencies of current spam filters against fraudulent (419) mails is not unrelated to the use by spammers of good-word attacks, topic drifts, parasitic spamming, wrong categorization and recategorization of electronic mails by e-mail clients and of course the fuzzy factors of greed and gullibility on the part of the recipients who responds to fraudulent spam mail offers. In this paper, we establish that mail token manipulations remain, above any other tactics, the most potent tool used by Nigerian scammers to fool statistical spam filters. While hoping that the uncovering of these manipulative evidences will prove useful in future antispam research, our findings also sensitize spam filter developers on the need to inculcate within their antispam architecture robust modules that can deal with the identified camouflages

Identifying and Profiling Radical Reviewer Collectives in Digital Product Reviews

Ecommerce sites are flooded with spam reviews and opinions. People are usually hired to impede or promote particular brands by writing extremely negative or positive reviews. It is usually performed in groups. Various studies have been conducted to identify and scan those spam groups. However, there is still a knowledge gap when it comes to detecting groups targeting a brand, instead of products only. In this study, we conducted a systematic review of recent studies related to detection of extremist reviewer groups. Most of the researchers have extracted these groups with a data mining approach over brand similarities so that users are clustered. This study is an attempt to detect spammers with various models tested by various reviewers. This study presents proven conceptual models and algorithms which have been presented in previous studies to compute the spamming level of extremist reviewers in ecommerce sites and online marketplace