An Effective Defense against Intrusive Web Advertising

Abstract

Intrusive Web advertising such as pop-ups and animated layer ads, which distract the user from reading or navigating through the main content of Web pages, is being perceived as annoying by an increasing number of users. As a response to the growing amount of extraneous content on today's Web and due to the lack of regulations imposed on abusive advertisers the author discusses the pros and cons of ad blocking, explores the different types of Web advertisements currently available and presents Quero, a novel Web browser-based content filter which implements a rule-based classifier that exploits, for example, hints present in the URL in order to classify objects as ads. Additionally, the author conducts a Web study to characterize online ads and measure the effectiveness of his solution against a manual classification. As a result, it is shown that a surprisingly small number of rules is sufficient to block almost all ads on the Web.