The algorithmic persuasion framework in online communication: conceptualization and a future research agenda

Zarouali, Brahim; Boerman, Sophie C.; Voorveld, Hilde A. M.; Noort, Guda van

doi:10.1108/intr-01-2021-0049

Cited by 18 publications

(3 citation statements)

References 111 publications

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“…Several limitations to our research are suggested. Firstly, there is the need to generalise our ML models to other domains and communication contexts (Zarouali et al , 2022). Relevant work (Ajzen, 1991; Chalaguine et al , 2019; Hadoux and Hunter, 2019) highlights that content of a message is important in predicting user responses and that people choose to believe arguments that align with their preferences.…”

Section: Discussionmentioning

confidence: 99%

“…This coalescence presents many opportunities to integrate PC and ML, both to discover more about the effects of PC and to better tailor products and services to individuals. Technologies that currently use PC include AI agents, user profiling and predictive models, with applications in fields such as business, education, health and psychology (Shumanov et al, 2021;Wang et al, 2019;Yang et al, 2019;Zarouali et al, 2022). PC and the use of linguistic tools of influence, namely, persuasion techniques are well known and extensively studiedfor a review, see (Dillard and Pfau, 2002;O'Keefe, 2015;Stiff and Mongeau, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Persuasive communication systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques

Braca

Dondio

2023

JSIT

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Purpose 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. Design/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). Findings 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 may be important in modelling persuasion. Research 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 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 – potentially translating into higher economic returns. Originality/value 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Persuasive communication systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques

Braca

Dondio

2023

JSIT

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“…Il s'agit d'un outil d'aide à la conception de Nudges technologiques développé par Carabon (2020) basé sur un système de cartes d'idéation fournissant des connaissances préalables sur le type d'interventions possibles, telles que les triggers. Les organisations à caractère technique s'appuient davantage sur des Frameworks issus de l'informatique persuasive (Zarouali et al, 2022). Ainsi, il apparait que dans la conception des Nudges-IA, on ne se limite pas à construire des architectures de choix.…”

Section: Au-delà Des Nudges Une Variété De Méthodes Non-triviales Pou...unclassified

Éthique du couplage Nudges-IA : enjeux épistémologiques et sociotechniques des Nudges IA dans les interactions humain-IA

ARRUABARRENA,

NESVIJEVSKAIA

2024

Interfaces Numériques

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Avec l’essor de l’économie comportementale, les Nudges se sont largement répandus dans divers aspects de la société et du numérique. Récemment, les progrès combinés des technologies de l’IA et des connaissances scientifiques, notamment en psychologie, en sciences humaines et sociales et en neurosciences, ont donné naissance à un nouveau phénomène appelé « l’Hypernudging ». Ce dernier se distingue par un changement d’échelle dans sa capacité à agir sur les décisions des individus. L’objectif de cette recherche, menée selon une approche socio-anthropologique, est d’avoir une meilleure compréhension des questions éthiques que soulève ce couplage des Nudges et de l’IA dans les interactions humain-IA. Dans cette perspective, nous avons réalisé une revue de littérature approfondie sur le sujet et mené une enquête exploratoire auprès d’un échantillon composé d’étudiants et de professionnels évoluant dans différents secteurs d’activités tels que la banque, la finance, le marketing ou les médias ainsi qu’auprès de deux agences de communication spécialistes des Nudges. Il s’agissait d’examiner les enjeux épistémologiques et sociotechniques de l’Hypernudging et de les confronter au développement des Nudges IA dans les organisations pour en dégager des pistes de recherche pour leur régulation éthique.

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Psychologically Informed Design of Energy Recommender Systems: Are Nudges Still Effective in Tailored Choice Environments?

Starke,

Willemsen

2024

Human–Computer Interaction Series

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The algorithmic persuasion framework in online communication: conceptualization and a future research agenda

Cited by 18 publications

References 111 publications

Persuasive communication systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques

Persuasive communication systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques

Éthique du couplage Nudges-IA : enjeux épistémologiques et sociotechniques des Nudges IA dans les interactions humain-IA

Psychologically Informed Design of Energy Recommender Systems: Are Nudges Still Effective in Tailored Choice Environments?

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