ARGUMENT MINING AND ITS APPLICATIONS IN POLITICAL DEBATES

Doctoral thesis (2022)

Presidential debates are significant moments in the history of presidential campaigns. In these debates, candidates are challenged to discuss the main contemporary and historical issues in the country and ... [more ▼]

Presidential debates are significant moments in the history of presidential campaigns. In these debates, candidates are challenged to discuss the main contemporary and historical issues in the country and attempt to persuade the voters to their benefit. These debates offer a legitimate ground for argumentative analysis to investigate political discourse argument structure and strategy. The recent advances in machine learning and Natural Language Processing (NLP) algorithms with the rise of deep learning have revolutionized many natural language applications, and argument analysis from textual resources is no exception. This dissertation targets argument mining from political debates data, a platform rifled with the arguments put forward by politicians to convince a general public in voting for them and discourage them from being appealed by the other candidates. The main contributions of the thesis are: i) Creation, release and reliability assessment of a valuable resource for argumentation research. ii) Implementation of a complete argument mining pipeline applying cutting-edge technologies in NLP research. iii) Launching of a demo tool for argumentative analysis of political debates. The original dataset is composed of the transcripts of 41 presidential election debates in the U.S. from 1960 to 2016. Beside argument extraction from political debates, this research also aims at investigating the practical applications of argument structure extraction, such as fallacious argument classification and argument retrieval. In order to apply supervised machine learning and NLP methods to the data, an excessive annotation study has been conducted on the data and led to the creation of a unique dataset with argument structures composed of argument components (i.e., claim and premise) and argument relations (i.e., support and attack). This dataset includes also another annotation layer with six fallacious argument categories and 14 sub-categories annotated on the debates. The final dataset is annotated with 32,296 argument components (i.e., 16,982 claims and 15,314 premises) and 25,012 relations (i.e., 3,723 attacks and 21,289 supports), and 1628 fallacious arguments. As the methodological approach, a complete argument mining pipeline is designed and implemented, composed of the two main stages of argument component detection and argument relation prediction. Each stage takes advantage of various NLP models outperforming standard baselines in the area, with an average F-score of 0.63 for argument components classification and 0.68 for argument relation classification. Additionally, DISPUTool, an argumentative analysis online tool, is developed as proof-of-concept. DISPUTool incorporates two main functionalities. Firstly, it provides the possibility of exploring the arguments which exist in the dataset. And secondly, it allows for extracting arguments from text segments inserted by the user leveraging the embedded trained model. [less ▲]