{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:35:46Z","timestamp":1771958146512,"version":"3.50.1"},"reference-count":117,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:00:00Z","timestamp":1679875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSIT"],"published-print":{"date-parts":[[2023,6,12]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>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\u2019s means of persuasion, rhetorical devices, cognitive theories and Cialdini\u2019s principles, given their psychometric profile.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>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\u2019s profile (personality, belief system and demographic data).<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>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%\u201370%, 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 may be important in modelling persuasion.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>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 \u2013 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual \u2013 potentially translating into higher economic returns.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles\/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/jsit-07-2022-0166","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T01:59:50Z","timestamp":1679623190000},"page":"160-191","source":"Crossref","is-referenced-by-count":8,"title":["Persuasive communication systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques"],"prefix":"10.1108","volume":"25","author":[{"given":"Annye","family":"Braca","sequence":"first","affiliation":[]},{"given":"Pierpaolo","family":"Dondio","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2023,3,27]]},"reference":[{"issue":"2","key":"key2023060906190821900_ref001","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/0749-5978(91)90020-T","article-title":"The theory of planned behavior","volume":"50","year":"1991","journal-title":"Organizational Behavior and Human Decision Processes"},{"issue":"4","key":"key2023060906190821900_ref002","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1111\/j.1559-1816.2002.tb00236.x","article-title":"Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior","volume":"32","year":"2002","journal-title":"Journal of Applied Social Psychology"},{"key":"key2023060906190821900_ref003","volume-title":"Psy-Q: You Know Your IQ-Now Test Your Psychological Intelligence","year":"2014"},{"issue":"2","key":"key2023060906190821900_ref004","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/j.1467-6494.1995.tb00807.x","article-title":"Extraversion: a \u2018hidden\u2019 personality factor in coping?","volume":"63","year":"1995","journal-title":"Journal of Personality"},{"key":"key2023060906190821900_ref005","first-page":"107","article-title":"Exploring the links between persuasion, personality and mobility types in personalized mobility applications","volume-title":"Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors","year":"2017"},{"key":"key2023060906190821900_ref006","volume-title":"Selling Higher Education: Marketing and Advertising America\u2019s Colleges and Universities","year":"2008"},{"key":"key2023060906190821900_ref007","volume-title":"Rhetoric","author":"Aristotle","year":"2015"},{"key":"key2023060906190821900_ref008","volume-title":"The Rhetoric of Aristotle: An Expanded Translation with Supplementary Examples for Students of Composition and Public Speaking","year":"1960"},{"key":"key2023060906190821900_ref009","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.paid.2017.12.018","article-title":"Predicting the big 5 personality traits from digital footprints on social media: a meta-analysis","volume":"124","year":"2018","journal-title":"Personality and Individual Differences"},{"issue":"2","key":"key2023060906190821900_ref010","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1177\/0146167290162013","article-title":"Mood and persuasion: a cognitive response analysis","volume":"16","year":"1990","journal-title":"Personality and Social Psychology Bulletin"},{"issue":"2","key":"key2023060906190821900_ref011","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1177\/1077699018763307","article-title":"The digital architectures of social media: comparing political campaigning on Facebook, Twitter, Instagram, and snapchat in the 2016 US election","volume":"95","year":"2018","journal-title":"Journalism and Mass Communication Quarterly"},{"issue":"2","key":"key2023060906190821900_ref012","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.jbusres.2009.02.006","article-title":"Fear, guilt, and shame appeals in social marketing","volume":"63","year":"2010","journal-title":"Journal of Business Research"},{"key":"key2023060906190821900_ref013","first-page":"3121","article-title":"The balanced accuracy and its posterior distribution","year":"2010"},{"key":"key2023060906190821900_ref014","volume-title":"Feeling Good","year":"1981"},{"key":"key2023060906190821900_ref015","first-page":"399","article-title":"Rhetorical means of persuasion","volume-title":"Esseys on Aristotles Rhetoric","year":"1996"},{"key":"key2023060906190821900_ref016","first-page":"1557","article-title":"Impact of argument type and concerns in argumentation with a chatbot","year":"2019"},{"key":"key2023060906190821900_ref017","first-page":"499","article-title":"How vulnerable are you? 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