The detection of hate speech and fake news in political discourse is at the same time a crucial necessity for democratic societies and a challenge for several areas of study. However, most of the studies have focused on what is explicitly stated: false article information, language that expresses hatred, derogatory expressions. This paper argues that the explicit dimension of manipulation is only one – and the least problematic – of the risks of political discourse. The language of the unsaid is much more dangerous and incomparably more difficult to detect, hidden in different types of fallacies and inappropriate uses of emotive language. Through a threefold coding scheme based on the instruments of argumentation theory and pragmatics, a corpus of argumentative tweets published by 4 politicians (Matteo Salvini, Donald Trump, Jair Bolsonaro, and Joseph Biden) within 6 months from their taking office (corresponding to the official end of their election campaign) is analyzed, detecting the types of argument, the fallacies, and the uses and misuses of “emotive words.” This coding results in the argumentation profiles of the speakers, which are compared statistically to show their different implicit strategies and deceptive tactics.