# Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News sites

arXiv: https://arxiv.org/abs/2202.05079 · HTML: https://mlsanity.com/paper/2202.05079/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2022.

## Built on by (most cited first, 7 of 7)

- [On the Risk of Misinformation Pollution with Large Language Models](https://mlsanity.com/paper/2305.13661/) (2023)
- [Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors](https://mlsanity.com/paper/2403.09747/) (2024)
- [FNDaaS: Content-agnostic Detection of Fake News sites](https://mlsanity.com/paper/2212.06492/) (2022)
- [Out-of-distribution Rumor Detection via Test-Time Adaptation](https://mlsanity.com/paper/2403.17735/) (2024)
- [Four Shades of Life Sciences: A Dataset for Disinformation Detection in the Life Sciences](https://mlsanity.com/paper/2507.03488/) (2025)
- [Verify as You Go: An LLM-Powered Browser Extension for Fake News Detection](https://mlsanity.com/paper/2603.05519/) (2026)
- [DariMis: Harm-Aware Modeling for Dari Misinformation Detection on YouTube](https://mlsanity.com/paper/2603.22977/) (2026)

## Abstract (arXiv)

Fake news is an age-old phenomenon, widely assumed to be associated with political propaganda published to sway public opinion. Yet, with the growth of social media, it has become a lucrative business for Web publishers. Despite many studies performed and countermeasures proposed, unreliable news sites have increased in the last years their share of engagement among the top performing news sources. Stifling fake news impact depends on our efforts in limiting the (economic) incentives of fake news producers. In this paper, we aim at enhancing the transparency around these exact incentives, and explore: Who supports the existence of fake news websites via paid ads, either as an advertiser or an ad seller? Who owns these websites and what other Web business are they into? We are the first to systematize the auditing process of fake news revenue flows. We identify the companies that advertise in fake news websites and the intermediary companies responsible for facilitating those ad revenues. We study more than 2,400 popular news websites and show that well-known ad networks, such as Google and IndexExchange, have a direct advertising relation with more than 40% of fake news websites. Using a graph clustering approach on 114.5K sites, we show that entities who own fake news sites, also operate other types of websites pointing to the fact that owning a fake news website is part of a broader business operation.