  The spread of digital disinformation (aka "fake news") is arguably one of the
most significant threats on the Internet which can cause individual and
societal harm of large scales. The susceptibility to fake news attacks hinges
on whether Internet users perceive a fake news article/snippet to be legitimate
after reading it. In this paper, we attempt to garner an in-depth understanding
of users' susceptibility to text-centric fake news attacks via a
neuro-cognitive methodology. We investigate the neural underpinnings relevant
to fake/real news through EEG. We run an experiment with human users to pursue
a thorough investigation of users' perception and cognitive processing of
fake/real news. We analyze the neural activity associated with the fake/real
news detection task for different categories of news articles. Our results show
there may be no statistically significant or automatically inferable
differences in the way the human brain processes the fake vs. real news, while
marked differences are observed when people are subject to (real/fake) news vs.
resting state and even between some different categories of fake news. This
neuro-cognitive finding may help to justify users' susceptibility to fake news
attacks, as also confirmed from the behavioral analysis. In other words, the
fake news articles may seem almost indistinguishable from the real news
articles in both behavioral and neural domains. Our work serves to dissect the
fundamental neural phenomena underlying fake news attacks and explains users'
susceptibility to these attacks through the limits of human biology. We believe
this could be a notable insight for the researchers and practitioners
suggesting the human detection of fake news might be ineffective, which may
also have an adverse impact on the design of automated detection approaches
that crucially rely upon human labeling of text articles for building training
models
