  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.
