Understanding the Detection of View Fraud in Video Content Portals

While substantial effort has been devoted to understand fraudulent activity in traditional online advertising (search and banner), more recent forms such as video ads have received little attention. The understanding and identification of fraudulent activity (i.e., fake views) in video ads for advertisers, is complicated as they rely exclusively on the detection mechanisms deployed by video hosting portals. In this context, the development of independent tools able to monitor and audit the fidelity of these systems are missing today and needed by both industry and regulators. In this paper we present a first set of tools to serve this purpose. Using our tools, we evaluate the performance of the audit systems of five major online video portals. Our results reveal that YouTube's detection system significantly outperforms all the others. Despite this, a systematic evaluation indicates that it may still be susceptible to simple attacks. Furthermore, we find that YouTube penalizes its videos' public and monetized view counters differently, the former being more aggressive. This means that views identified as fake and discounted from the public view counter are still monetized. We speculate that even though YouTube's policy puts in lots of effort to compensate users after an attack is discovered, this practice places the burden of the risk on the advertisers, who pay to get their ads displayed.Comment: To appear in WWW 2016, Montr\'eal, Qu\'ebec, Canada. Please cite the conference version of this pape

Online Child Sex Solicitation: Exploring the Feasibility of a Research 'Sting'

A small scale test of the integrity of Internet Web 2.0 social network sites was undertaken over several weeks in 2007. The fictional identities of four female underage children where posted on three network sites and later introduced to relay chat forums in order to explore the impact of apparent vulnerability on potential selection of Internet victims. Only one of the three social network sites in the study recognised that the postings violated child protection policies and subsequently closed down the underage postings. Two basic identities were created: one that engendered a needy and vulnerable characterisation of a child while the other identity was created to represent a happy and attached child character. The number of contacts and suspicious contacts were monitored to test assumptions about child ‘vulnerability’ and risks of unwanted sexual solicitations. The characters created also included either an avatar and/or contact details. These variants of the experiment showed that the inclusion of an image or access details increased the likelihood of contacts, including suspicious contact regardless of ‘vulnerability’. This small experiment noted that although vulnerable children with additional cues maybe at more risk all children who posted details about themselves on social network sites faced the risk of contact by predators. The need for further research and better means of regulating such sites was suggested

Using Sentiment Analysis and Pattern Matching to Signal User Review Abnormalities

User opinions on websites like Amazon, Yelp, and TripAdvisor are a key input for consumers when figuring out what to purchase, or where and what to eat. This means that in order for such websites to provide a better service to their customers, they must guard against fake and targeted reviews. Detecting such users and reviews automatically is a very complex multi-step process, and there is no direct mechanism for solving the problem reliably. Multiple AI and Machine Learning algorithms are coupled together when examining user reviews in determining if a review is fake or not. In this project we propose one such mechanism, which examines past user reviews to detect abnormalities, if any, signaling that they should be looked at more thoroughly from more dimensions. We do so by combining existing sentiment analysis techniques and pattern matching. In order to gain more insight into a review, we break it down into sentences and produce a sentiment value for each one, allowing us to represent a review as a sentiment vector. The sentiment vector then allows us to match various sized tuples against other reviews from the user and compute abnormality scores