# The Rise and Fall of Fake News sites: A Traffic Analysis

arXiv: https://arxiv.org/abs/2103.09258 · HTML: https://mlsanity.com/paper/2103.09258/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [Combating Fake News: A Survey on Identification and Mitigation Techniques](https://mlsanity.com/paper/1901.06437/) (2019)
- [FLaaS: Federated Learning as a Service](https://mlsanity.com/paper/2011.09359/) (2020)
- [Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web](https://mlsanity.com/paper/1801.09288/) (2018)
- [An Emotional Analysis of False Information in Social Media and News Articles](https://mlsanity.com/paper/1908.09951/) (2019)
- [The Web Centipede: Understanding How Web Communities Influence Each Other Through the Lens of Mainstream and Alternative News Sources](https://mlsanity.com/paper/1705.06947/) (2017)
- [Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web](https://mlsanity.com/paper/1905.04260/) (2019)
- [If you are not paying for it, you are the product: How much do advertisers pay to reach you?](https://mlsanity.com/paper/1701.07058/) (2017)
- [No More Chasing Waterfalls: A Measurement Study of the Header Bidding Ad-Ecosystem](https://mlsanity.com/paper/1907.12649/) (2019)
- [Stop Tracking Me Bro! Differential Tracking Of User Demographics On Hyper-partisan Websites](https://mlsanity.com/paper/2002.00934/) (2020)
- [Detection and Analysis of 2016 US Presidential Election Related Rumors on Twitter](https://mlsanity.com/paper/1701.06250/) (2017)
- [WhoTracks .Me: Shedding light on the opaque world of online tracking](https://mlsanity.com/paper/1804.08959/) (2018)
- [Keeping out the Masses: Understanding the Popularity and Implications of Internet Paywalls](https://mlsanity.com/paper/1903.01406/) (2019)
- [User Tracking in the Post-cookie Era: How Websites Bypass GDPR Consent to Track Users](https://mlsanity.com/paper/2102.08779/) (2021)

## Built on by (most cited first, 3 of 3)

- [WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries](https://mlsanity.com/paper/2407.17468/) (2024)
- [Interpretable Fake News Detection with Topic and Deep Variational Models](https://mlsanity.com/paper/2209.01536/) (2022)
- [FNDaaS: Content-agnostic Detection of Fake News sites](https://mlsanity.com/paper/2212.06492/) (2022)

## Abstract (arXiv)

Over the past decade, we have witnessed the rise of misinformation on the Internet, with online users constantly falling victims of fake news. A multitude of past studies have analyzed fake news diffusion mechanics and detection and mitigation techniques. However, there are still open questions about their operational behavior such as: How old are fake news websites? Do they typically stay online for long periods of time? Do such websites synchronize with each other their up and down time? Do they share similar content through time? Which third-parties support their operations? How much user traffic do they attract, in comparison to mainstream or real news websites? In this paper, we perform a first of its kind investigation to answer such questions regarding the online presence of fake news websites and characterize their behavior in comparison to real news websites. Based on our findings, we build a content-agnostic ML classifier for automatic detection of fake news websites (i.e. accuracy) that are not yet included in manually curated blacklists.