  Over the past decade, fake news and misinformation have turned into a major
problem that has impacted different aspects of our lives, including politics
and public health. Inspired by natural human behavior, we present an approach
that automates the detection of fake news. Natural human behavior is to
cross-check new information with reliable sources. We use Natural Language
Processing (NLP) and build a machine learning (ML) model that automates the
process of cross-checking new information with a set of predefined reliable
sources. We implement this for Twitter and build a model that flags fake
tweets. Specifically, for a given tweet, we use its text to find relevant news
from reliable news agencies. We then train a Random Forest model that checks if
the textual content of the tweet is aligned with the trusted news. If it is
not, the tweet is classified as fake. This approach can be generally applied to
any kind of information and is not limited to a specific news story or a
category of information. Our implementation of this approach gives a $70\%$
accuracy which outperforms other generic fake-news classification models. These
results pave the way towards a more sensible and natural approach to fake news
detection.
