Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

Definition of Spam 2.0: New Spamming Boom

The most widely recognized form of spam is e-mail spam, however the term “spam” is used to describe similarabuses in other media and mediums. Spam 2.0 (or Web 2.0 Spam) is refereed to as spam content that is hosted on online Web 2.0 applications. In this paper: we provide a definition of Spam 2.0, identify and explain different entities within Spam 2.0, discuss new difficulties associated with Spam 2.0, outline its significance, and list possible countermeasure. The aim of this paper is to provide the reader with a complete understanding of this new form of spamming

From past to present: spam detection and identifying opinion leaders in social networks

On microblogging sites, which are gaining more and more users every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In some cases, in Turkey as well as in the rest of the world, it was noticed that events were published on microblogging sites before appearing in visual, audio and printed news sources. Thanks to the rapid flow of information in social networks, it can reach millions of people in seconds. In this context, social media can be seen as one of the most important sources of information affecting public opinion. Since the information in social networks became accessible, research started to be conducted using the information on the social networks. While the studies about spam detection and identification of opinion leaders gained popularity, surveys about these topics began to be published. This study also shows the importance of spam detection and identification of opinion leaders in social networks. It is seen that the data collected from social platforms, especially in recent years, has sourced many state-of-art applications. There are independent surveys that focus on filtering the spam content and detecting influencers on social networks. This survey analyzes both spam detection studies and opinion leader identification and categorizes these studies by their methodologies. As far as we know there is no survey that contains approaches for both spam detection and opinion leader identification in social networks. This survey contains an overview of the past and recent advances in both spam detection and opinion leader identification studies in social networks. Furthermore, readers of this survey have the opportunity of understanding general aspects of different studies about spam detection and opinion leader identification while observing key points and comparisons of these studies.This work is supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK) through grant number 118E315 and grant number 120E187. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of TUBITAK.Publisher's VersionEmerging Sources Citation Index (ESCI)Q4WOS:00080858480001

A Study on Opinion Spamming: Fake Consumer Review Detection

Online audits are the most important wellsprings of data about client feelings and are considered the columns on which the standing of an association is assembled. From a client's viewpoint, audit data is vital to settle on an appropriate choice with respect to an online buy. Surveys are for the most part thought to be a fairminded assessment of a person's very own involvement in an item, however, the fundamental truth about these audits recounts an alternate story. Spammers abuse these audit stages unlawfully on account of impetuses engaged with composing counterfeit surveys, subsequently attempting to acquire a bit of leeway over contenders bringing about an unstable development of assessment spamming. This training is known as Opinion Spam, where spammers control and toxic substance surveys for benefit or gain. In the event that one sees numerous positive audits of the item, one is probably going to purchase the item. Notwithstanding, in the event that one sees many negative surveys, he/she will in all probability pick another item. Positive suppositions can bring about huge monetary benefits and additionally popularities for associations and people. This, sadly, offers great motivating forces for input spam. Most of the momentum research has zeroed in on regulated learning strategies, which require named information, a shortage with regards to online survey spam. Examination of techniques for Big Data is of revenue, since there are a huge number of online audits, with a lot seriously being produced every day. Until now, we have not discovered any papers that review the impacts of Big Data examination for survey spam identification. The essential objective of this paper is to give a solid and farreaching similar investigation of flow research on identifying audit spam utilizing different AI procedures and to devise a strategy for directing further examination

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

Review Spam Detection Using Machine Learning Techniques

Nowadays with the increasing popularity of internet, online marketing is going to become more and more popular. This is because, a lot of products and services are easily available online. Hence, reviews about these all products and services are very important for customers as well as organizations. Unfortunately, driven by the will for profit or promotion, fraudsters used to produce fake reviews. These fake reviews written by fraudsters prevent customers and organizations reaching actual conclusions about the products. Hence, fake reviews or review spam must be detected and eliminated so as to prevent deceptive potential customers. In our work, supervised and semi-supervised learning technique have been applied to detect review spam. The most apt data sets in the research area of review spam detection has been used in proposed work. For supervised learning, we try to obtain some feature sets from different automated approaches such as LIWC, POS Tagging, N-gram etc., that can best distinguish the spam and non-spam reviews. Along with these features sentiment analysis, data mining and opinion mining technique have also been applied. For semi-supervised learning, PU-learning algorithm is being used along with six different classifiers (Decision Tree, Naive Bayes, Support Vector Machine, k-Nearest Neighbor, Random Forest, Logistic Regression) to detect review spam from the available data set. Finally, a comparison of proposed technique with some existing review spam detection techniques has been done

Addressing the new generation of spam (Spam 2.0) through Web usage models

New Internet collaborative media introduce new ways of communicating that are not immune to abuse. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content or a manipulated Wiki page, are examples of new the generation of spam on the web, referred to as Web 2.0 Spam or Spam 2.0. Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications.The current literature does not address Spam 2.0 in depth and the outcome of efforts to date are inadequate. The aim of this research is to formalise a definition for Spam 2.0 and provide Spam 2.0 filtering solutions. Early-detection, extendibility, robustness and adaptability are key factors in the design of the proposed method.This dissertation provides a comprehensive survey of the state-of-the-art web spam and Spam 2.0 filtering methods to highlight the unresolved issues and open problems, while at the same time effectively capturing the knowledge in the domain of spam filtering.This dissertation proposes three solutions in the area of Spam 2.0 filtering including: (1) characterising and profiling Spam 2.0, (2) Early-Detection based Spam 2.0 Filtering (EDSF) approach, and (3) On-the-Fly Spam 2.0 Filtering (OFSF) approach. All the proposed solutions are tested against real-world datasets and their performance is compared with that of existing Spam 2.0 filtering methods.This work has coined the term ‘Spam 2.0’, provided insight into the nature of Spam 2.0, and proposed filtering mechanisms to address this new and rapidly evolving problem