An Empirical Inquiry into Surveillance Capitalism: Web Tracking
- URL: http://arxiv.org/abs/2508.07454v2
- Date: Tue, 12 Aug 2025 09:52:53 GMT
- Title: An Empirical Inquiry into Surveillance Capitalism: Web Tracking
- Authors: Nils Bonfils,
- Abstract summary: This paper analyzes patterns and trends in web tracking data to establish empirical evidence of Surveillance Capitalism's extraction mechanisms.<n>Our findings reveal Google's omnipresent position on the web, a three-tier stratification among companies in the surveillance space, and evidence suggesting an evolution of tracking techniques to evade detection.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The modern web is increasingly characterized by the pervasiveness of Surveillance Capitalism. This investigation employs an empirical approach to examine this phenomenon through the web tracking practices of major tech companies -- specifically Google, Apple, Facebook, Amazon, and Microsoft (GAFAM) -- and their relation to financial performance indicators. Using longitudinal data from WhoTracks.Me spanning from 2017 to 2025 and publicly accessible SEC filings, this paper analyzes patterns and trends in web tracking data to establish empirical evidence of Surveillance Capitalism's extraction mechanisms. Our findings reveal Google's omnipresent position on the web, a three-tier stratification among GAFAM companies in the surveillance space, and evidence suggesting an evolution of tracking techniques to evade detection. The investigation further discusses the social and environmental costs of web tracking and how alternative technologies, such as the Gemini protocol, offer pathways to challenge the extractive logic of this new economic order. By closely examining surveillance activities, this research contributes to an ongoing effort to better understand the current state and future trajectory of Surveillance Capitalism.
Related papers
- SoK: Web3 RegTech for Cryptocurrency VASP AML/CFT Compliance [14.435108642393319]
This paper examines how blockchain-native RegTech solutions leverage distributed ledger properties to enable novel compliance capabilities.<n>We develop three organizing the Web3 RegTech domain: a regulatory paradigm evolution framework across ten dimensions, a compliance protocol taxonomy encompassing five verification layers, and a RegTech lifecycle framework spanning preventive, real-time, and investigative phases.<n>Our analysis reveals critical gaps between academic innovation and industry deployment, alongside persistent challenges in cross-chain tracking, DeFi interaction analysis, privacy protocol monitoring, and scalability.
arXiv Detail & Related papers (2025-12-31T14:31:29Z) - A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms [0.0]
This study investigates fraud detection in ride hailing platforms through Graph Neural Networks (GNNs)<n>By analyzing prevalent fraudulent activities, the research highlights and compares the existing work related to fraud detection.<n>Also, the paper highlights addressing class imbalance and fraudulent camouflage.
arXiv Detail & Related papers (2025-12-29T13:26:06Z) - SoK: Advances and Open Problems in Web Tracking [71.54586748169943]
Web tracking is a pervasive and opaque practice that enables personalized advertising, and conversion tracking.<n>Web tracking is undergoing a once-in-a-generation transformation driven by shifts in the advertising industry, the adoption of anti-tracking countermeasures by browsers, and the growing enforcement of emerging privacy regulations.<n>This Systematization of Knowledge (SoK) aims to consolidate and synthesize this wide-ranging research, offering a comprehensive overview of the technical mechanisms, countermeasures, and regulations that shape the modern and rapidly evolving web tracking landscape.
arXiv Detail & Related papers (2025-06-16T23:30:54Z) - From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories [3.323388021979584]
Malicious URLs persistently threaten the cybersecurity ecosystem, by either deceiving users into divulging private data or distributing harmful payloads to infiltrate host systems.<n>This review systematically analyzes methods from traditional blacklisting to advanced deep learning approaches.<n>Unlike prior surveys, we propose a novel modality-based taxonomy that categorizes existing works according to their primary data modalities.
arXiv Detail & Related papers (2025-04-23T06:23:18Z) - Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions [51.43521977132062]
Money laundering is a financial crime that obscures the origin of illicit funds.<n>The proliferation of mobile payment platforms and smart IoT devices has significantly complicated anti-money laundering investigations.<n>This paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML.
arXiv Detail & Related papers (2025-03-13T05:19:44Z) - Surveillance Capitalism Revealed: Tracing The Hidden World Of Web Data Collection [0.0]
This study investigates the mechanisms of Surveillance Capitalism, focusing on personal data transfer during web navigation and searching.<n>We present concrete evidence of data harvesting practices and propose strategies for enhancing data protection and transparency.
arXiv Detail & Related papers (2024-12-23T19:55:20Z) - A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures [50.987594546912725]
Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations.
This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures.
arXiv Detail & Related papers (2024-03-31T12:44:48Z) - Graph Mining for Cybersecurity: A Survey [61.505995908021525]
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society.
Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities.
With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
arXiv Detail & Related papers (2023-04-02T08:43:03Z) - Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application
of Machine Learning-based Forensics [5.617291981476445]
The paper analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques.
It shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution.
arXiv Detail & Related papers (2022-06-07T16:22:55Z) - 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) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z)
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.