2,533 research outputs found
adPerf: Characterizing the Performance of Third-party Ads
Monetizing websites and web apps through online advertising is widespread in
the web ecosystem. The online advertising ecosystem nowadays forces publishers
to integrate ads from these third-party domains. On the one hand, this raises
several privacy and security concerns that are actively studied in recent
years. On the other hand, given the ability of today's browsers to load dynamic
web pages with complex animations and Javascript, online advertising has also
transformed and can have a significant impact on webpage performance. The
performance cost of online ads is critical since it eventually impacts user
satisfaction as well as their Internet bill and device energy consumption.
In this paper, we apply an in-depth and first-of-a-kind performance
evaluation of web ads. Unlike prior efforts that rely primarily on adblockers,
we perform a fine-grained analysis on the web browser's page loading process to
demystify the performance cost of web ads. We aim to characterize the cost by
every component of an ad, so the publisher, ad syndicate, and advertiser can
improve the ad's performance with detailed guidance. For this purpose, we
develop an infrastructure, adPerf, for the Chrome browser that classifies page
loading workloads into ad-related and main-content at the granularity of
browser activities (such as Javascript and Layout). Our evaluations show that
online advertising entails more than 15% of browser page loading workload and
approximately 88% of that is spent on JavaScript. We also track the sources and
delivery chain of web ads and analyze performance considering the origin of the
ad contents. We observe that 2 of the well-known third-party ad domains
contribute to 35% of the ads performance cost and surprisingly, top news
websites implicitly include unknown third-party ads which in some cases build
up to more than 37% of the ads performance cost
Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites
Dark patterns are user interface design choices that benefit an online
service by coercing, steering, or deceiving users into making unintended and
potentially harmful decisions. We present automated techniques that enable
experts to identify dark patterns on a large set of websites. Using these
techniques, we study shopping websites, which often use dark patterns to
influence users into making more purchases or disclosing more information than
they would otherwise. Analyzing ~53K product pages from ~11K shopping websites,
we discover 1,818 dark pattern instances, together representing 15 types and 7
broader categories. We examine these dark patterns for deceptive practices, and
find 183 websites that engage in such practices. We also uncover 22 third-party
entities that offer dark patterns as a turnkey solution. Finally, we develop a
taxonomy of dark pattern characteristics that describes the underlying
influence of the dark patterns and their potential harm on user
decision-making. Based on our findings, we make recommendations for
stakeholders including researchers and regulators to study, mitigate, and
minimize the use of these patterns.Comment: 32 pages, 11 figures, ACM Conference on Computer-Supported
Cooperative Work and Social Computing (CSCW 2019
Toward ubiquitous searching
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