From Blocking to Breaking: Evaluating the Impact of Adblockers on Web Usability
- URL: http://arxiv.org/abs/2410.23504v1
- Date: Wed, 30 Oct 2024 23:25:07 GMT
- Title: From Blocking to Breaking: Evaluating the Impact of Adblockers on Web Usability
- Authors: Ritik Roongta, Mitchell Zhou, Ben Stock, Rachel Greenstadt,
- Abstract summary: We aim to assess the extent of web breakages caused by adblocking on live sites using automated tools.
The study also outlines the challenges and limitations encountered when measuring web breakages in real-time.
- Score: 14.498659516878718
- License:
- Abstract: Recent years have seen a sharp rise in adblocker use, driven by increased web tracking and personalized ads. However, a significant issue for adblocker users is the web breakages they encounter, which worsens their browsing experience and often leads them to turn off their adblockers. Despite efforts by filter list maintainers to create rules that minimize these breakages, they remain a common issue. Our research aims to assess the extent of web breakages caused by adblocking on live sites using automated tools, attempting to establish a baseline for these disruptions. The study also outlines the challenges and limitations encountered when measuring web breakages in real-time. The current automated crawler's inability to consistently navigate a vast array of websites, combined with the unpredictable nature of web content, makes this research particularly difficult. We have identified several key findings related to web breakages in our preliminary study, which we intend to delve deeper into in future research.
Related papers
- MASKDROID: Robust Android Malware Detection with Masked Graph Representations [56.09270390096083]
We propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware.
We introduce a masking mechanism into the Graph Neural Network based framework, forcing MASKDROID to recover the whole input graph.
This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks.
arXiv Detail & Related papers (2024-09-29T07:22:47Z) - Accessibility Issues in Ad-Driven Web Applications [3.9531869396416344]
Third-party advertisements (ads) are a vital revenue source for free web services, but they introduce significant accessibility challenges.
We conduct the first large-scale investigation of 430K website elements, including nearly 100K ad elements, to understand the accessibility of ads on websites.
arXiv Detail & Related papers (2024-09-27T09:50:06Z) - SINBAD: Saliency-informed detection of breakage caused by ad blocking [7.384101553309326]
Filter-list maintainers could benefit from automated breakage detection tools.
SINBAD is an automated breakage detector that improves the accuracy over the state of the art by 20%.
arXiv Detail & Related papers (2024-05-08T16:35:06Z) - User Attitudes to Content Moderation in Web Search [49.1574468325115]
We examine the levels of support for different moderation practices applied to potentially misleading and/or potentially offensive content in web search.
We find that the most supported practice is informing users about potentially misleading or offensive content, and the least supported one is the complete removal of search results.
More conservative users and users with lower levels of trust in web search results are more likely to be against content moderation in web search.
arXiv Detail & Related papers (2023-10-05T10:57:15Z) - Exposing and Addressing Security Vulnerabilities in Browser Text Input
Fields [22.717150034358948]
We perform a comprehensive analysis of the security of text input fields in web browsers.
We find that browsers' coarse-grained permission model violates two security design principles.
We uncover two vulnerabilities in input fields, including the alarming discovery of passwords in plaintext.
arXiv Detail & Related papers (2023-08-30T21:02:48Z) - The Devil is in the Details: Analyzing the Lucrative Ad Fraud Patterns of the Online Ad Ecosystem [2.1456348289599134]
Bad actors have found ways to circumvent restrictions, and generate substantial revenue that can support websites with objectionable or even illegal content.
We show how identifier pooling can redirect ad revenues from reputable domains to notorious domains serving objectionable content.
We publish a Web monitoring service that enhances the transparency of supply chains and business relationships between publishers and ad networks.
arXiv Detail & Related papers (2023-06-14T10:28:07Z) - Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating
Noisy Samples and Utilizing Hard Ones [60.07027312916081]
We propose a novel approach for removing irrelevant samples from real-world web images during training.
Our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance.
arXiv Detail & Related papers (2021-01-23T03:58:10Z) - Learning to Infer User Hidden States for Online Sequential Advertising [52.169666997331724]
We propose our Deep Intents Sequential Advertising (DISA) method to address these issues.
The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states)
arXiv Detail & Related papers (2020-09-03T05:12:26Z) - Do Interruptions Pay Off? Effects of Interruptive Ads on Consumers
Willingness to Pay [79.9312329825761]
We present the results of a study designed to measure the impact of interruptive advertising on consumers willingness to pay for products bearing the advertiser's brand.
Our results contribute to the research on the economic impact of advertising, and introduce a method of measuring actual (as opposed to self-reported) willingness to pay in experimental marketing research.
arXiv Detail & Related papers (2020-05-14T09:26:57Z) - A4 : Evading Learning-based Adblockers [44.149991991963795]
A4 is a tool that crafts adversarial samples of ads to evade AdGraph.
We show that A4 can bypass AdGraph about 60% of the time.
We envision the algorithmic framework proposed in A4 is also promising in improving adversarial attacks against other learning-based web applications.
arXiv Detail & Related papers (2020-01-29T18:13:12Z) - Block the blocker: Studying the effects of Anti Ad-blocking [4.615921064099383]
We discuss at length data collection of top websites in the world, Germany, DACH region and news category.
Our paper also discusses how Anti Ad-blockers impact the economic, legal and ethical usage in Germany along with the recent changes in usage.
arXiv Detail & Related papers (2020-01-26T10:58:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.