Fake
Activity
Market
Observation system of
Unethical
Services

Our project seeks to expose and reduce the prevalence of fake online activities in Europe, such as influencer marketing fraud, phishing scams, and counterfeit product sales. By combining advanced technologies, including large language models and cybersecurity tools, the project map and monitor Fake Activity Shops (FAS) operating across European countries. This interdisciplinary effort involves collaboration among social media platforms, regulatory bodies, academics, and the general public. The goal is to offer new tools, methodologies, and policy recommendations to protect online spaces and improve trust in digital ecosystems.

FAMOUS maps

This map is based on collection and analysis of hundreds of FAS and it shows the distribution of fake activity markets across Europe using different metrics. It is based on data collected during 2025 and 2026.

Use the year selector in the centre to switch between data snapshots. On the left, choose the metric you want to explore. Hover over a country to see detailed values. Hover over the question mark icon next to the selector to see a short explanation [click to see full screen].

Market renewal and transnationality [link]

FAM trends over four metrics:

Turnover intensity — how frequently new shops appear and existing ones disappear.
Survival rate — the share of shops that remain active over time.
Average country breadth — how many countries a typical shop operates in.
Market size — the total number of active fake activity shops.

Cross-country market matrix [link]

Map of how fake activity markets are shared across countries. Each cell is the level of overlap between two countries (how many FAS operate in both countries).

Countries that are closely connected tend to share the same providers, forming cross-border market clusters (use sort button to explore clusters).

Use this view to identify how the market spreads across Europe.

Infrastructure-network chart [link]

Map of how FAS are connected through shared infrastructure.

Each bubble represents a group of FAS that are likely operated by the same entity, based on shared IP addresses, hosting, or other technical signals. These are networked shops rather than independent ones.

Use this view to identify large-scale providers and understand how FAS are organised into networks.

Dataset & Papers

Available on Kaggle

The FAMOUS dataset captures the supply side of the fake activity across Europe.

It includes fake activity shops (websites selling likes, followers, comments, etc.), their search visibility, pricing, and technical signals.

The dataset enables cross-country analysis of market structure, pricing patterns, and how easily these services can be discovered online.

Research papers:

“Measuring the Discoverability of Fake Activity Shops Across Europe”, ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2026) [under review].
The paper focuses on how easily Fake Activity Shops can be found through major search engines and introduces the Discoverability Index as a cross-country metric of market exposure. Its key finding is that Fake Activity Shops remain highly visible in search results, with especially strong differences between Google, Bing, and DuckDuckGo and a replicated East–West divide within Europe. The paper also translates these results into policy recommendations, arguing for a harmonised, metrics-driven approach to risk assessment, stronger Commission-level coordination, and improved cross-border enforcement mechanisms to reduce visibility asymmetries in the single market.
“Accountability Gaps in the Fake Follower Market”, ACM Conference on Fairness, Accountability, and Transparency (FAccT 2026) [accepted].
This paper develops the project’s main fairness–transparency–accountability framework for analysing the Fake Activity Market. It combines monitoring data, forensic signals, expert interviews, and legal analysis to show that the market remains visible and economically organised, while responsibility for intervention is fragmented across search engines, platforms, payment providers, and regulators. The paper formulates four main policy directions: recognition of FAM-related harms within DSA systemic-risk assessments, longitudinal market observation, supply-chain intervention through actors such as hosting and payment providers, and redress mechanisms for market actors harmed by manipulated engagement metrics.
“FAMOUS Dataset: Fake Activity Market Observation system of Unethical Services”, AAAI Conference on Web and Social Media (ICWSM 2026) [under review].
This paper presents the dataset produced by the project and explains its structure, scope, and intended reuse. Its main dissemination contribution is to make the project’s empirical output more visible and reusable by linking the paper to the public Kaggle release and the accompanying interactive map.
“From Fake Likes to Real Risks: A Mixed-Method Analysis of Fake Social Activity Shops in Europe”, USENIX Security Symposium (USENIX Security 2026) [under review].
The paper provides a broader mixed-method analysis of the European Fake Activity Shop ecosystem by combining large-scale web analysis, forensic inspection, and expert interviews. Its main contribution is to systematise the risks associated with Fake Activity Shops by showing that they create a two-directional risk structure: on the one hand, deceptive service fraud and manipulation of social proof, and on the other hand, direct cybersecurity threats such as redirections, suspicious scripts, privacy risks, and related technical harms.
"Fake Activity Markets as Hybrid Threat Vector: Implications for EU Social and Cyber Resilience", European Journal of Risk Regulation [accepted].
The article examines Fake Activity Markets as both enablers of coordinated inauthentic behaviour and sources of direct cybersecurity risk. It argues that these markets should be understood as part of the EU's broader hybrid threat landscape, with implications for social resilience, cyber resilience, and future regulatory responses under the Union's evolving security agenda.
“Between Sovereignty, Security, and Standards: Social Media Governance in the EU and China”, book chapter in EU and China Investment and Trade Relations: Legal Challenges of the Digital-Tech Age [accepted].
The chapter examines how the EU and China, despite very different legal and political systems, have developed increasingly similar approaches to social media governance. It shows that in both cases platform regulation now extends beyond content moderation to include user conduct, data governance, infrastructure, and algorithmic systems, shaped by the respective logics of digital sovereignty and cyber sovereignty.