  In this paper, we combine two independent detection methods for identifying
fake news: the algorithm VAGO uses semantic rules combined with NLP techniques
to measure vagueness and subjectivity in texts, while the classifier FAKE-CLF
relies on Convolutional Neural Network classification and supervised deep
learning to classify texts as biased or legitimate. We compare the results of
the two methods on four corpora. We find a positive correlation between the
vagueness and subjectivity measures obtained by VAGO, and the classification of
text as biased by FAKE-CLF. The comparison yields mutual benefits: VAGO helps
explain the results of FAKE-CLF. Conversely FAKE-CLF helps us corroborate and
expand VAGO's database. The use of two complementary techniques (rule-based vs
data-driven) proves a fruitful approach for the challenging problem of
identifying fake news.
