mlsanityData to 7 Oct 2026

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

arXiv 2103.09258 ↗First posted 16 Mar 2021cs.SI cs.CR cs.LGcited by 3

Not on the map. Why?

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 49 references that are in the map. First posted on arXiv 16 Mar 2021.

Builds on

15 of 49 references.

References · 15

  1. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 334
  2. 2017Beyond News Contents: The Role of Social Context for Fake News Detectioncited by 91
  3. 2019Combating Fake News: A Survey on Identification and Mitigation Techniquescited by 33
  4. 2020FLaaS: Federated Learning as a Servicecited by 22
  5. 2018Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Webcited by 18
  6. 2019An Emotional Analysis of False Information in Social Media and News Articlescited by 12
  7. 2017The Web Centipede: Understanding How Web Communities Influence Each Other Through the Lens of Mainstream and Alternative News Sourcescited by 11
  8. 2019Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Webcited by 6
  9. 2017If you are not paying for it, you are the product: How much do advertisers pay to reach you?cited by 3
  10. 2019No More Chasing Waterfalls: A Measurement Study of the Header Bidding Ad-Ecosystemcited by 3
  11. 2020Stop Tracking Me Bro! Differential Tracking Of User Demographics On Hyper-partisan Websitescited by 3
  12. 2017Detection and Analysis of 2016 US Presidential Election Related Rumors on Twittercited by 1
3 more
  1. 2018WhoTracks .Me: Shedding light on the opaque world of online trackingcited by 1
  2. 2019Keeping out the Masses: Understanding the Popularity and Implications of Internet Paywallscited by 1
  3. 2021User Tracking in the Post-cookie Era: How Websites Bypass GDPR Consent to Track Userscited by 1

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1.

3 papers.

  1. 2024WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queriescited by 26
  2. 2022Interpretable Fake News Detection with Topic and Deep Variational Modelscited by 3
  3. 2022FNDaaS: Content-agnostic Detection of Fake News sitescited by 1