Strategies for Automatic Detection of Fallacious Arguments in Political Speeches during Electoral Campaigns in Mexico

Abstract

This study proposes a machine learning approach to automatically detect "appeal to emotion" fallacies. The objective is to establish a set of elements that enable the application of fallacy mining. Our method uses a lexicon of emotions to distinguish valid arguments from fallacies, employing Support Vector Machine and Multilayer Perceptron models. The Multilayer Perceptron obtained an F1 score of 0.60 in identifying fallacies. Based on our analysis, we suggest using lexical dictionaries to effectively identify "appeal to emotion" fallacies.