{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/misinformation-detection-using-persuasive","title":"Using Persuasive Writing Strategies to Explain and Detect Health Misinformation","arxiv_id":"2211.05985","date":"2022-11-11","proceeding":null,"authors":["Danial Kamali","Joseph Romain","Huiyi Liu","Wei Peng","Jingbo Meng","Parisa Kordjamshidi"],"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.","url_abs":"https://arxiv.org/abs/2211.05985v4","url_pdf":"https://arxiv.org/pdf/2211.05985v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"misinformation-detection-using-persuasive","repo_url":"https://github.com/hlr/misinformation-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"fake-news-detection","task_name":"Fake News Detection"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"misinformation","task_name":"Misinformation"},{"task_slug":null,"task_name":"Persuasive Writing Strategy Detection"},{"task_slug":"persuasive-writing-strategy-detection-level-1","task_name":"Persuasive Writing Strategy Detection Level-1"},{"task_slug":"persuasive-writing-strategy-detection-level-2","task_name":"Persuasive Writing Strategy Detection Level-2"},{"task_slug":"persuasive-writing-strategy-detection-level-3","task_name":"Persuasive Writing Strategy Detection Level-3"},{"task_slug":"persuasive-writing-strategy-detection-level-4","task_name":"Persuasive Writing Strategy Detection Level-4"},{"task_slug":"prompt-engineering","task_name":"Prompt Engineering"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roberta","method_name":"RoBERTa"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[{"slug":"persuasive-writing-strategy","name":"Persuasive Writing Strategy","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/fake-news-detection-on-rawfc","task":"Fake News Detection","dataset":"RAWFC","model":"Persuasive Writing Strategy","rank_in_archive_order":1,"of":6,"metrics":{"F1":"55.8"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}