Ad blocking

Abstract: Content and services which are offered for free on the Internet are primarily monetized through online advertisement. This business model relies on the implicit agreement between content providers and users where viewing ads is the price for the "free" content. This status quo is not acceptable to all users, however, as manifested by the rise of ad-blocking plugins which are available for all popular Web browsers. Indeed, ad-blockers have the potential to substantially disrupt the widely established business model of "free" content, currently one of the core elements on which the Web is built.In this work, we shed light on how users interact with ads. We show how to leverage the functionality of AdBlock Plus, one of the most popular ad-blockers to identify ad traffic from passive network measurements. We complement previous work, which focuses on active measurements, by characterizing ad-traffic in the wild, i.e., as seen in a residential broadband network of a major European ISP. Finally, we assess the prevalence of ad-blockers in this particular network and discuss possible implications for content providers and ISPs.

Abstract: Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between a...

Abstract: User demand for blocking advertising and tracking online is large and growing. Existing tools, both deployed and described in research, have proven useful, but lack either the completeness or robustness needed for a general solution. Existing detection approaches generally focus on only one aspect of advertising or tracking (e.g. URL patterns, code structure), making existing approaches susceptible to evasion.In this work we present AdGraph, a novel graph-based machine learning approach for detecting advertising and tracking resources on the web. AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources. Because AdGraph considers many aspects of the context a network request takes place in, it is less susceptible to the single-factor evasion techniques that flummox existing approaches.We evaluate AdGraph on the Alexa top-10K websites, and find that it is highly accurate, able to replicate the labels of human-generated filter lists with 95.33% accuracy, and can even identify many mistakes in filter lists. We implement AdGraph as a modification to Chromium. AdGraph adds only minor overhead to page loading and execution, and is actually faster than stock Chromium on 42% of websites and AdBlock Plus on 78% of websites. Overall, we conclude that AdGraph is both accurate enough and performant enough for online use, breaking comparable or fewer websites than popular filter list based approaches.

Abstract: Fueled by advancing technologies that continually expand Web data tracking and aggregating capabilities, online advertising has become increasingly personalized and pervasive. This trend is largely responsible for a growing number of consumers (more than 615 million worldwide) choosing to install ad-blocking software on their computers and mobile devices. As a result, U.S. publishers and advertisers estimate that ad blockers cost them more than $28 billion in revenue in the first half of 2017, and this figure is forecast to exceed $35 billion by 2020. Rooted in a theoretical foundation of psychological reactance theory (PRT), the present study examines key cognitive and affective factors driving consumers to reject personalized advertising messages and install ad-blocking software. A structural equation analysis reveals privacy-related threats, along with benefits rooted in relevance and rewards, moderated by the type of data being used to personalize advertising messages, are contributing to this...