  Over the last years, there has been an unprecedented proliferation of fake
news. As a consequence, we are more susceptible to the pernicious impact that
misinformation and disinformation spreading can have in different segments of
our society. Thus, the development of tools for automatic detection of fake
news plays and important role in the prevention of its negative effects. Most
attempts to detect and classify false content focus only on using textual
information. Multimodal approaches are less frequent and they typically
classify news either as true or fake. In this work, we perform a fine-grained
classification of fake news on the Fakeddit dataset, using both unimodal and
multimodal approaches. Our experiments show that the multimodal approach based
on a Convolutional Neural Network (CNN) architecture combining text and image
data achieves the best results, with an accuracy of 87%. Some fake news
categories such as Manipulated content, Satire or False connection strongly
benefit from the use of images. Using images also improves the results of the
other categories, but with less impact. Regarding the unimodal approaches using
only text, Bidirectional Encoder Representations from Transformers (BERT) is
the best model with an accuracy of 78%. Therefore, exploiting both text and
image data significantly improves the performance of fake news detection.
