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Lookup NU author(s): Dr Maksim Kalameyets, Jindi WangORCiD, Professor Ben FarrandORCiD, Dr Lei ShiORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Purchased likes, followers, views, comments, and reviews constitute a professionalised “fake-engagement-as-a-service” market that distorts social proof, skews consumer choice, undermines legitimate advertising, and exposes users to fraud and privacy risks. Despite these effects, the market remains difficult to observe at scale, and responsibility is fragmented across platforms, search engines, intermediaries, and regulators. This paper introduces an empirical mixed-methods framework that renders the fake social activity market observable and auditable. Drawing on 15 expert interviews and forensic analysis of EU-based FAS, we first identify the principal risks and harms associated with this market. We then develop an automated monitoring pipeline that measures (i) discoverability – the prominence of FAS in search engine results across jurisdictions; (ii) market structure – the services offered, pricing strategies, and operational features; and (iii) the cross-border ecosystem – the degree of provider overlap across countries as an indicator of market integration. Finally, we assess how the Digital Services Act (DSA) can be operationalised to address these risks. Across two scans of 24 countries covering 30,000 search results, we identify more than 2,000 FAS and show that fake engagement services remain readily accessible via major search engines, with marked regional variation. We observe substantial price dispersion, extensive reliance on mainstream payment processors, and cross-border clustering shaped by linguistic and cultural similarity. While many downstream harms fall within existing regulatory frameworks, legal obligations remain unclear, producing an accountability gap that the DSA has the capacity but not yet the guidance to address.
Author(s): Kalameyets M, Owens R, Hammouchi H, Sergeeva A, Hoehn S, Jalilzade E, Wang J, Patel S, Aidinlis S, Farrand B, Lenzini G, Shi L
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT '26)
Year of Conference: 2026
Pages: 5864-5885
Online publication date: 25/06/2026
Acceptance date: 02/03/2026
Date deposited: 07/06/2026
Publisher: Association for Computing Machinery
URL: https://doi.org/10.1145/3805689.3806758
DOI: 10.1145/3805689.3806758
Data Access Statement: The FAS dataset collected and analysed in this paper is publicly available on Kaggle via the link: https://doi.org/10.34740/kaggle/dsv/15353445
Library holdings: Search Newcastle University Library for this item
ISBN: 9798400725968
