Identifying disinformation websites using infrastructure features

Austin Hounsel, Jordan Holland, Ben Kaiser, Kevin Borgolte, Nicholas G. Feamster, Jonathan Mayer

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Platforms have struggled to keep pace with the spread of disinformation. Current responses like user reports, manual analysis, and third-party fact checking are slow and difficult to scale, and as a result, disinformation can spread unchecked for some time after being created. Automation is essential for enabling platforms to respond rapidly to disinformation. In this work, we explore a new direction for automated detection of disinformation websites: infrastructure features. Our hypothesis is that while disinformation websites may be perceptually similar to authentic news websites, there may also be significant non-perceptual differences in the domain registrations, TLS/SSL certificates, and web hosting configurations. Infrastructure features are particularly valuable for detecting disinformation websites because they are available before content goes live and reaches readers, enabling early detection. We demonstrate the feasibility of our approach on a large corpus of labeled website snapshots. We also present results from a preliminary real-time deployment, successfully discovering disinformation websites while highlighting unexplored challenges for automated disinformation detection.

Original languageEnglish (US)
StatePublished - 2020
Event10th USENIX Workshop on Free and Open Communications on the Internet, FOCI 2020, co-located with USENIX Security 2020 - Virtual, Online
Duration: Aug 11 2020 → …

Conference

Conference10th USENIX Workshop on Free and Open Communications on the Internet, FOCI 2020, co-located with USENIX Security 2020
CityVirtual, Online
Period8/11/20 → …

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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