  This paper addresses the problem of fake news detection in Spanish using
Machine Learning techniques. It is fundamentally the same problem tackled for
the English language; however, there is not a significant amount of publicly
available and adequately labeled fake news in Spanish to effectively train a
Machine Learning model, similarly to those proposed for the English language.
Therefore, this work explores different training strategies and architectures
to establish a baseline for further research in this area. Four datasets were
used, two in English and two in Spanish, and four experimental schemes were
tested, including a baseline with classical Machine Learning models, trained
and validated using a small dataset in Spanish. The remaining schemes include
state-of-the-art Deep Learning models trained (or fine-tuned) and validated in
English, trained and validated in Spanish, and fitted in English and validated
with automatic translated Spanish sentences. The Deep Learning architectures
were built on top of different pre-trained Word Embedding representations,
including GloVe, ELMo, BERT, and BETO (a BERT version trained on a large corpus
in Spanish). According to the results, the best strategy was a combination of a
pre-trained BETO model and a Recurrent Neural Network based on LSTM layers,
yielding an accuracy of up to 80%; nonetheless, a baseline model using a Random
Forest estimator obtained similar outcomes. Additionally, the translation
strategy did not yield acceptable results because of the propagation error;
there was also observed a significant difference in models performance when
trained in English or Spanish, mainly attributable to the number of samples
available for each language.
