Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks
Abstract
Asynchronous messaging is leading human-machine interaction due to the boom of mobile devices and social networks. The recent release of dedicated APIs from messaging platforms boosted the development of computer programs able to conduct conversations, (i.e., chatbots), which have been adopted in several domain-specific contexts. This paper proposes SMAG: a chatbot framework supporting a smoking cessation program (JDF) deployed on a social network. In particular, it details the single-agent implementation, the campaign results, a multi-agent design for SMAG enabling the modelization of personalized behavior and user profiling, and highlighting of coupling chatbot technology with and multi-agent systems.
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Of the 15 studies, 4 (27%) assessed efficacy of AI chatbots in smoking cessation.
“Out of the 15 studies, 4 (27%) studies [23-25,27] assessed the efficacy of AI chatbots in smoking cessation.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [27]
Calvaresi et al reported that 28.9% of participants completed their smoking cessation goal 3 months after the last cigarette, which was 10% higher than the previous edition of the smoking cessation program without chatbot support.
“Calvaresi et al [27] reported that 28.9% of the participants completed their smoking cessation goal 3 months after the last cigarette. This result was 10% higher than that of the previous edition of the smoking cessation program, which did not include chatbot support.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [27]
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Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review.
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“%) reinforced motivation through personalized messages based on TTM (DigiQuit [23]), scoreboards and trackers of milestones (SFA [24]), and motivational messages (CASC [25] and SMAG [27]).”
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- Authors
- Calvaresi, D., Calbimonte, J.-P., Dubosson, F., Najjar, A., & Schumacher, M.
- Affiliations
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- Umeå University (SE)
- Venue
- IEEE/WIC/ACM International Conference on Web Intelligence
- Publisher
- ACM
- Published
- 2019-10-14
- Pages
- 286-292
- OpenAlex
- W2980406972
- Cited by
- 43
- References
- 35
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 5 · 2022: 6 · 2021: 14 · 2020: 2
- FWCI
- 34.49
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/WIC/ACM International Conference on Web Intelligence (publishedVersion)
- ArODES (HES-SO (https://www.hes-so.ch/)) (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- chatbots, smoking cessation, multi-agent systems, user profiling, health behavior change
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