Twitter is an instrument used not only for sharing public or personal information, but also for persuading the audience. While specific platforms and software have been developed for analyzing macro-analytical data, and specific studies have focused on the linguistic dimension of the tweets, the argumentative dimension of the latter is unexplored to this date. This paper intends to propose a method grounded on the tools advanced in argumentation theory for capturing, coding, and assessing the different argumentative dimensions of the messages (...) posted on Twitter, focusing on the types of argument communicated, the quality of their premises, and the fallacies committed – including the use of unshared presuppositions and emotive words. This method is applied to a corpus of 843 tweets published by the Italian Minister of the Interior, Mr. Matteo Salvini, from the date of his appointment to the beginning of his campaign for the European elections. The quantitative data provide general indications for detecting the strategies that characterize the argumentative profile of Salvini, which are then analyzed qualitatively. (shrink)

This paper uses argument diagrams, argumentation schemes, and some tools from formal argumentation systems developed in artificial intelligence to build a graph-theoretic model of relevance shown to be applicable as a practical method for helping a third party judge issues of relevance or irrelevance of an argument in real examples. Examples used to illustrate how the method works are drawn from disputes about relevance in natural language discourse, including a criminal trial and a parliamentary debate.