Papers › Using Persuasive Writing Strategies to Explain and Detect Health Misinformation

Using Persuasive Writing Strategies to Explain and Detect Health Misinformation

11 Nov 2022arXiv:2211.05985archive 2025-07-28

Danial Kamali, Joseph Romain, Huiyi Liu, Wei Peng, Jingbo Meng, Parisa Kordjamshidi

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.

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Code

hlr/misinformation-detection officialmentioned in paperpytorch report

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Tasks

Fake News Detection Language Modelling Misinformation Persuasive Writing Strategy Detection Level-1 Persuasive Writing Strategy Detection Level-2 Persuasive Writing Strategy Detection Level-3 Persuasive Writing Strategy Detection Level-4 Prompt Engineering Text Classification text-classification

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Datasets

Introduced by this paper, per the archive.

Persuasive Writing Strategy

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Fake News Detection	RAWFC	Persuasive Writing Strategy	F1	55.8	#1 of 6	Archive leaderboard	report

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Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection RoBERTa Softmax Weight Decay WordPiece

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