Argumentation schemes, fallacies, and evidence in politicians' argumentative tweets-A coded dataset

Area of Science:

• Political Science
• Computational Linguistics
• Communication Studies

Background:

• Political discourse analysis often overlooks the immediate post-election period.
• Argumentation theory and pragmatics provide frameworks for analyzing political communication.
• Digital platforms like Twitter are crucial for disseminating political messages.

Purpose of the Study:

• To create a coded database of argumentative tweets from four prominent politicians.
• To analyze the types of arguments, fallacies, and emotive language used by politicians.
• To provide a resource for comparative studies and training in discourse analysis.

Main Methods:

• A corpus of 2657 tweets from Matteo Salvini, Donald Trump, Jair Bolsonaro, and Joe Biden was collected.
• Tweets were coded using a threefold method based on argumentation theory and pragmatics.
• Coding involved identifying argument types, fallacies (including lack of evidence/false presupposition), and emotive language.

Main Results:

• The database categorizes tweets by argument types, fallacies, and emotive language.
• Each tweet is coded for up to two argument types, two fallacies, and three emotive words.
• The coding scheme allows for detailed analysis of political rhetoric.

Conclusions:

• The coded database serves as a valuable resource for studying political communication.
• This systematic approach enables comparative analysis of politicians' discourse.
• The corpus can be used to train researchers in analyzing political tweets.