Using Persuasive Writing Strategies to Explain and Detect Health Misinformation

TL;DR

This paper introduces a new annotation scheme for persuasive strategies in health misinformation, creating a dataset and demonstrating that leveraging these strategies improves detection accuracy and explainability in AI models.

Contribution

It proposes a novel task of annotating persuasive strategies in health misinformation and evaluates their use in enhancing model performance and interpretability.

Findings

Persuasive strategies improve misinformation detection accuracy.

Annotated dataset of health misinformation with persuasive tactics.

Strategies enhance model explainability and trustworthiness.

Abstract

Nowadays, the spread of misinformation is a prominent problem in society. Our research focuses on aiding the automatic identification of misinformation by analyzing the persuasive strategies employed in textual documents. We introduce a novel annotation scheme encompassing common persuasive writing tactics to achieve our objective. Additionally, we provide a dataset on health misinformation, thoroughly annotated by experts utilizing our proposed scheme. Our contribution includes proposing a new task of annotating pieces of text with their persuasive writing strategy types. We evaluate fine-tuning and prompt-engineering techniques with pre-trained language models of the BERT family and the generative large language models of the GPT family using persuasive strategies as an additional source of information. We evaluate the effects of employing persuasive strategies as intermediate labels in the context of misinformation detection. Our results show that those strategies enhance accuracy and improve the explainability of misinformation detection models. The persuasive strategies can serve as valuable insights and explanations, enabling other models or even humans to make more informed decisions regarding the trustworthiness of the information.