  Online users today are exposed to misleading and propagandistic news articles
and media posts on a daily basis. To counter thus, a number of approaches have
been designed aiming to achieve a healthier and safer online news and media
consumption. Automatic systems are able to support humans in detecting such
content; yet, a major impediment to their broad adoption is that besides being
accurate, the decisions of such systems need also to be interpretable in order
to be trusted and widely adopted by users. Since misleading and propagandistic
content influences readers through the use of a number of deception techniques,
we propose to detect and to show the use of such techniques as a way to offer
interpretability. In particular, we define qualitatively descriptive features
and we analyze their suitability for detecting deception techniques. We further
show that our interpretable features can be easily combined with pre-trained
language models, yielding state-of-the-art results.
