5 research outputs found
Actual persuasiveness : Impact of personality, age and gender on message type susceptibility
The authors would like to acknowledge and thank all the volunteers who participated in the experiment and provided helpful comments. The first author is funded by an EPSRC doctoral training grant.Postprin
Towards Computational Persuasion via Natural Language Argumentation Dialogues
Computational persuasion aims to capture the human ability to persuade through argumentation for applications such as behaviour change in healthcare (e.g. persuading people to take more exercise or eat more healthily). In this paper, we review research in computational persuasion that incorporates domain modelling (capturing arguments and counterarguments that can appear in a persuasion dialogues), user modelling (capturing the beliefs and concerns of the persuadee), and dialogue strategies (choosing the best moves for the persuader to maximize the chances that the persuadee is persuaded). We discuss evaluation of prototype systems that get the userās counterarguments by allowing them to select them from a menu. Then we consider how this work might be enhanced by incorporating a natural language interface in the form of an argumentative chatbot
Is ArguMessage effective? : A critical evaluation of the persuasive message generation system
This paper describes an investigation into the effectiveness of ArguMessage, a system that uses argumentation schemes and limited user input to semi-automatically generate persuasive messages encouraging behaviour change that follow specific argumentation patterns. We conducted user studies in the domains of healthy eating and email security to investigate its effectiveness. Our results show that ArguMessage in general supported users in generating messages based on the argumentation schemes. However, there were some issues in particular with copying the example messages, and some system improvements need to be made. Participants were generally satisfied with the messages produced, with the exception of those produced by two schemes (āArgument from memory with goalā and āArgument from values with goalā) which were removed after the first study