[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123690-en":3,"doc-seo-123690-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123690,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Persuasive Communication Systems - a machine learning approach to predict the effect of linguistic styles and persuasion techniques","Prediction underpins targeted online advertising, where better-than-random estimates can improve economic returns. This study applies machine learning to forecast how individual respondents are influenced by textual linguistic styles and persuasion techniques, grounded in Aristotle’s and Cialdini’s frameworks and informed by psychometric profiles. Using survey data from 1,022 participants, models learn from personality measures, dysfunctional attitude scale features, and demographic inputs. Results show emotion-based variables are crucial, with best accuracy between 53%–70%.","Technological University Dublin  \nARROW@TU Dublin  \n\n| Articles | School of Computer Sciences |\n| --- | --- |\n| 2023\u003Cbr>Persuasive Communication Systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques\u003Cbr>Annye Braca Pierpaolo Dondio\u003Cbr>Follow this and additional works at: [https://arrow.tudublin.ie/scschcomart](https://arrow.tudublin.ie/scschcomart)\u003Cbr> Part of the Digital Communications and Networking Commons |  |\n\nThis work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License. Funder: Science Foundation Ireland (Grant 13/RC/2106) and the ADAPT Centre ([www.adaptcentre.ie](www.adaptcentre.ie)) at Technological University Dublin ([www.tudublin.ie](www.tudublin.ie)) .  \nThe current issue and full text archive of this journal is available on Emerald Insight at:  \n[https://www.emerald.com/insight/1328-7265.htm](https://www.emerald.com/insight/1328-7265.htm)  \nPersuasive communication systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques  \nAnnye Braca and Pierpaolo Dondio  \nSchool of Computer Science, Technological University Dublin, Dublin, Ireland  \nAbstract  \nPurpose – Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.  \nDesign/methodology/approach – A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).  \nFindings – The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they maybe important in modelling persuasion.  \nResearch limitations/implications – In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features. Practical implications – The findings of this study indicate that leveraging richer profiling dat","cbCaig2zVmPP2owv","https://ap.wps.com/l/cbCaig2zVmPP2owv","pdf",2001309,1,33,"English","en",105,"# Abstract\n## Purpose\n## Design/methodology/approach\n## Findings\n## Research limitations/implications\n## Practical implications\n## Originality/value","[{\"question\":\"What problem does the study address in persuasive communication systems?\",\"answer\":\"The study targets prediction of how individuals respond to different linguistic styles and persuasion techniques, aiming for performance beyond random guessing in targeted online advertising contexts.\"},{\"question\":\"What data and measures are used to build the machine learning models?\",\"answer\":\"The models use a participant profiling input set including personality measures, the dysfunctional attitude scale (DAS), and demographic data, collected from 1,022 survey respondents.\"},{\"question\":\"Which modeling inputs and persuasion techniques improve prediction performance?\",\"answer\":\"Incorporating emotion-based variables improves predictive accuracy, and models perform best for responses to statements using rhetorical devices and flattery. Adding DAS scale features also improves performance, while authority and social proof are less predictable.\"}]","Persuasive Communication Systems - a machine learning approach to predict the effect of linguistic styles and persuasion techniques | PDF",1785818027,83,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"persuasive-communication-systems-a-machine-learning-approach-to-predict-the-effect-of-linguistic-styles-and-persuasion-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/persuasive-communication-systems-a-machine-learning-approach-to-predict-the-effect-of-linguistic-styles-and-persuasion-techniques/123690/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in persuasive communication systems?","Question",{"text":75,"@type":76},"The study targets prediction of how individuals respond to different linguistic styles and persuasion techniques, aiming for performance beyond random guessing in targeted online advertising contexts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and measures are used to build the machine learning models?",{"text":80,"@type":76},"The models use a participant profiling input set including personality measures, the dysfunctional attitude scale (DAS), and demographic data, collected from 1,022 survey respondents.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling inputs and persuasion techniques improve prediction performance?",{"text":84,"@type":76},"Incorporating emotion-based variables improves predictive accuracy, and models perform best for responses to statements using rhetorical devices and flattery. 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