Amalie Pauli Brogaard
Abstract:
Misinformation and propaganda are recognised as a major threat to people’s judgement and informed decision making in health, politics and news consumption. The spread of misinformation relating to the Covid19 epidemic is just one prominent example. It is not only wrong facts that constitute a threat, but also the language used which can lead to deception and misleading of people. To address the misinformation threat and empower readers confronted with enormous amounts of information, we propose a new data science methodology for the computational analysis of rhetoric in text.
While rhetoric, the art of persuasion, is an ancient discipline, its computational analysis, regarding persuasion techniques, is still in its infancy. We propose a data science project on computational modelling and automatic detection of persuasion techniques at the intersection of Natural Language Processing (NLP) and Machine Learning. We posit that detecting and highlighting persuasion techniques enables critical reading of a text, thereby reducing the impact of manipulative and disingenuous content.
Knowing and understanding fallacies and rhetorical tricks may also help to make stronger, valid arguments in a variety of texts. Moreover, we expect rhetorical information to be beneficial to other semantic language processing tasks and we, therefore, devise approaches to capture and transfer rhetorical knowledge to models for such tasks.
This project will contribute novel models for detecting persuasion techniques, as well as new transfer learning methods for utilising rhetorical knowledge to benefit other semantic text analysis methods.