
The way we relay factual information and the way we present deceptive information as truth differs from the perspective of argumentation. 
In this paper, we explore whether these differences can be exploited to detect deceptive political news in English.
We do this by training a model to detect different kinds of argumentation in online news text.
We use sentence embeddings extracted from an argumentation type classification model as features for a deceptive news classifier.
This deceptive news classification model leverages the sequence of argumentation types within an article to determine whether it is credible or deceptive.
Our approach outperforms other state-of-the-art models while having lower variance.
Finally, we use the output of our argumentation model to analyze the differences between credible and deceptive news based on the distribution of argumentation types across the articles.
Results of this analysis indicate that credible political news presents statements supported by a variety of argumentation types, while deceptive news relies on anecdotes and testimonial.
