Online Advertising is a Regrettable Necessity: On the Dangers of Pay-Walling the Web

• URL: http://arxiv.org/abs/2409.00026v2
• Date: Wed, 4 Sep 2024 21:42:30 GMT
• Title: Online Advertising is a Regrettable Necessity: On the Dangers of Pay-Walling the Web
• Authors: Yonas Kassa,
• Abstract summary: We show that 135 countries with a total population estimate of 6.56 billion people cannot afford a scenario of a fully paywalled web. We call for further research and policy initiatives to keep the web open and more inclusive with a sustainable business model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exponential growth of the web and its benefits can be attributed largely to its open model where anyone with internet connection can access information on the web for free. This has created unprecedented opportunities for various members of society including the most vulnerable, as recognized by organizations such as the UN. This again can be attributed to online advertising, which has been the main financier to the open web. However, recent trends of paywalling information and services on the web are creating imminent dangers to such open model of the web, inhibiting access for the economically vulnerable, and eventually creating digital segregation. In this paper, we argue that this emerging model lacks sustainability, exacerbates digital divide, and might lead to collapse of online advertising. We revisit the ad-supported open web business model and demonstrate how global users actually pay for the ads they see. Using data on GNI (gross national income) per capita and average paywall access costs, we established a simple income-paywall expenditure gap baseline. With this baseline we show that 135 countries with a total population estimate of 6.56 billion people cannot afford a scenario of a fully paywalled web. We further discuss how a mixed model of the so-called "premium services" creates digital segregation and poses danger to online advertising ecosystem. Finally, we call for further research and policy initiatives to keep the web open and more inclusive with a sustainable business model.

Related papers

• The Illusion of Anonymity: Uncovering the Impact of User Actions on Privacy in Web3 Social Ecosystems [11.501563549824466]
  We investigate the nuanced dynamics between user engagement on Web3 social platforms and the consequent privacy concerns. We scrutinize the widespread phenomenon of fabricated activities, which encompasses the establishment of bogus accounts aimed at mimicking popularity. We highlight the urgent need for more stringent privacy measures and ethical protocols to navigate the complex web of social exchanges.
  arXiv  Detail & Related papers  (2024-05-22T06:26:15Z)
• Tag Your Fish in the Broken Net: A Responsible Web Framework for Protecting Online Privacy and Copyright [30.05760947688919]
  This paper introduces a user-controlled consent tagging framework for online data. With this framework, users have the ability to tag their online data at the time of transmission, and subsequently, they can track and request the withdrawal of consent for their data from the data holders.
  arXiv  Detail & Related papers  (2023-10-11T21:56:16Z)
• 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)
• Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
  We design an intelligent approach to online privacy protection that leverages supervised learning. By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
  arXiv  Detail & Related papers  (2023-04-06T05:20:16Z)
• Web 3.0: The Future of Internet [53.234101208024335]
  Web 3.0 is a decentralized Web architecture that is more intelligent and safer than before. Web 3.0 is capable of addressing web data ownership according to distributed technology. It will optimize the internet world from the perspectives of economy, culture, and technology.
  arXiv  Detail & Related papers  (2023-03-23T15:37:42Z)
• Web3: The Next Internet Revolution [50.16560061003771]
  Next internet revolution: Web3 is going to open new opportunities for traditional social models. Decentralized finance will be global, and open with financial inclusiveness for unbanked people. Several worthwhile future research directions of Web3 are discussed.
  arXiv  Detail & Related papers  (2023-03-22T23:37:43Z)
• Lessons from the AdKDD'21 Privacy-Preserving ML Challenge [57.365745458033075]
  A prominent proposal at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays. To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD'21. A key finding is that learning models on large, aggregated data in the presence of a small set of unaggregated data points can be surprisingly efficient and cheap.
  arXiv  Detail & Related papers  (2022-01-31T11:09:59Z)
• How resilient is the Open Web to the COVID-19 pandemic? [0.0]
  Despite its importance for society, it is unknown how the COVID-19 pandemic is affecting the Open Web. We study the impact of the pandemic in the financial backbone of the Open Web, the online advertising business. We analyze the distribution of the Open Web composition across business categories and its evolution during the COVID-19 pandemic.
  arXiv  Detail & Related papers  (2021-07-30T10:32:19Z)
• Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release [52.504589728136615]
  We develop a data poisoning method by which publicly released data can be minimally modified to prevent others from train-ing models on it. We demonstrate the success of our approach onImageNet classification and on facial recognition.
  arXiv  Detail & Related papers  (2021-02-16T19:12:34Z)
• Explainable Patterns: Going from Findings to Insights to Support Data Analytics Democratization [60.18814584837969]
  We present Explainable Patterns (ExPatt), a new framework to support lay users in exploring and creating data storytellings. ExPatt automatically generates plausible explanations for observed or selected findings using an external (textual) source of information.
  arXiv  Detail & Related papers  (2021-01-19T16:13:44Z)

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.