  With the current shift in the mass media landscape from journalistic rigor to
social media, personalized social media is becoming the new norm. Although the
digitalization progress of the media brings many advantages, it also increases
the risk of spreading disinformation, misinformation, and malformation through
the use of fake news. The emergence of this harmful phenomenon has managed to
polarize society and manipulate public opinion on particular topics, e.g.,
elections, vaccinations, etc. Such information propagated on social media can
distort public perceptions and generate social unrest while lacking the rigor
of traditional journalism. Natural Language Processing and Machine Learning
techniques are essential for developing efficient tools that can detect fake
news. Models that use the context of textual data are essential for resolving
the fake news detection problem, as they manage to encode linguistic features
within the vector representation of words. In this paper, we propose a new
approach that uses document embeddings to build multiple models that accurately
label news articles as reliable or fake. We also present a benchmark on
different architectures that detect fake news using binary or multi-labeled
classification. We evaluated the models on five large news corpora using
accuracy, precision, and recall. We obtained better results than more complex
state-of-the-art Deep Neural Network models. We observe that the most important
factor for obtaining high accuracy is the document encoding, not the
classification model's complexity.
