Blackmarket-Driven Collusion on Online Media: A Survey

Abstract

Online media platforms have enabled users to connect with individuals and organizations, and share their thoughts. Other than connectivity, these platforms also serve multiple purposes, such as education, promotion, updates, and awareness. Increasing, the reputation of individuals in online media (aka social reputation ) is thus essential these days, particularly for business owners and event managers who are looking to improve their publicity and sales. The natural way of gaining social reputation is a tedious task, which leads to the creation of unfair ways to boost the reputation of individuals artificially. Several online blackmarket services have developed a thriving ecosystem with lucrative offers to attract content promoters for publicizing their content online. These services are operated in such a way that most of their inorganic activities are going unnoticed by the media authorities, and the customers of the blackmarket services are less likely to be spotted. We refer to such unfair ways of bolstering social reputation in online media as collusion . This survey is the first attempt to provide readers a comprehensive outline of the latest studies dealing with the identification and analysis of blackmarket-driven collusion in online media. We present a broad overview of the problem, definitions of the related problems and concepts, the taxonomy of the proposed approaches, and a description of the publicly available datasets and online tools, and we discuss the outstanding issues. We believe that collusive entity detection is a newly emerging topic in anomaly detection and cyber-security research in general, and the current survey will provide readers with an easy-to-access and comprehensive list of methods, tools, and resources proposed so far for detecting and analyzing collusive entities on online media.

Exploring optimal pathways for enterprise procurement management systems based on fast neural modeling and semantic segmentation

“…Artificial intelligence, particularly neural network methodologies, have demonstrated remarkable efficacy in the detection of collusion behavior. Neural networks, intricate computational models that emulate the functioning of the human brain, exhibit exceptional prowess in data processing and pattern recognition, rendering them potent instruments for discerning collusion behavior [ 3 ]. These neural networks employ a multi-layered neuron structure and possess the capacity to autonomously discern, extract, and comprehend patterns and features within data through extensive learning from voluminous data samples.…”

A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns

“…Related work has highlighted particularly questionable practices, such as astroturfing (i.e., hidden coordinated information campaigns that mimic genuine user behavior by incentivizing agents to spread information online) for consumers (Kauppila and Soliman 2022;Lee, Tamilarasan, and Caverlee 2013) and citizens (Schoch et al 2022). Dutta and Chakraborty (2021) have studied blackmarket services that are employed by "collusive" users to inflate the popularity of their online account and get appraisals for their content, while spam has been a topic of intensive research over the years (e.g., Benevenuto et al 2010;Yardi et al 2009).…”

A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns

“…Related work has highlighted particularly questionable practices, such as astroturfing (i.e., hidden coordinated information campaigns that mimic genuine user behavior by incentivizing agents to spread information online) for consumers (Kauppila and Soliman 2022;Lee, Tamilarasan, and Caverlee 2013) and citizens (Schoch et al 2022). Dutta and Chakraborty (2021) have studied blackmarket services that are employed by "collusive" users to inflate the popularity of their online account and get appraisals for their content, while spam has been a topic of intensive research over the years (e.g., Benevenuto et al 2010;Yardi et al 2009).…”