Chalaguine, Lisa Andreevna;
(2023)
Computational Persuasion using Chatbots based on Crowdsourced Argument Graphs & Concerns.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
As computing becomes involved in every sphere of life, so too is persuasion a target for applying computer-based solutions. Conversational agents, also known as chatbots, are versatile tools that have the potential of being used as agents in dialogical argumentation systems where the chatbot acts as the persuader and the human agent as the persuadee and thereby offer a costeffective and scalable alternative to in-person consultations To allow the user to type his or her argument in free-text input (as opposed to selecting arguments from a menu) the chatbot needs to be able to (1) “understand” the user’s concern he or she is raising in their argument and (2) give an appropriate counterargument that addresses the user’s concern. In this thesis I describe how to (1) acquire arguments for the construction of the chatbot’s knowledge base with the help of crowdsourcing, (2) how to automatically identify the concerns that arguments address, and (3) how to construct the chatbot’s knowledge base in the form of an argument graph that can be used during persuasive dialogues with users. I evaluated my methods in four case studies that covered several domains (physical activity, meat consumption, UK University Fees and COVID-19 vaccination). In each case study I implemented a chatbot that engaged in argumentative dialogues with participants and measured the participants’ change of stance before and after engaging in a chat with the bot. In all four case studies the chatbot showed statistically significant success persuading people to either consider changing their behaviour or to change their stance.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Computational Persuasion using Chatbots based on Crowdsourced Argument Graphs & Concerns |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163521 |
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