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Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood
Open accessAbstract
From past research it is well known that social exclusion has detrimental consequences for mental health. To deal with these adverse effects, socially excluded individuals frequently turn to other humans for emotional support. While chatbots can elicit social and emotional responses on the part of the human interlocutor, their effectiveness in the context of social exclusion has not been investigated. In the present study, we examined whether an empathic chatbot can serve as a buffer against the adverse effects of social ostracism. After experiencing exclusion on social media, participants were randomly assigned to either talk with an empathetic chatbot about it (e.g., "I'm sorry that this happened to you") or a control condition where their responses were merely acknowledged (e.g., "Thank you for your feedback"). Replicating previous research, results revealed that experiences of social exclusion dampened the mood of participants. Interacting with an empathetic chatbot, however, appeared to have a mitigating impact. In particular, participants in the chatbot intervention condition reported higher mood than those in the control condition. Theoretical, methodological, and practical implications, as well as directions for future research are discussed.
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“Relative to chatbots that do not express empathy, chatbots that express empathy lead to more favorable ratings of companionship, i.e., the activities done together are perceived as more enjoyable or exciting (Boucher et al. 2021; Leite et al. 2013), and better mood after experiencing social exclusion (De Gennaro, Krumhuber, and Lucas 2020).”
From AI Companions Reduce Loneliness (2024) · cites this paper as De Gennaro, Krumhuber, and Lucas 2020
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“CAs with advanced empathetic skills have improved user affinities and experiences [77,78].”
From Conversational Agent Interventions for Mental Health Problems: Systematic Review and Meta-analysis of Randomized Controlled Trials (2023) · cites this paper as [77]
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Metadata
- Authors
- de Gennaro, M., Krumhuber, E. G., & Lucas, G.
- Affiliations
- University College London (GB)
- University of Southern California (US)
- Corresponding author
- Krumhuber, Eva G.
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2020-01-23
- Volume
- 10
- Pages
- 3061-3061
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC6989433
- OpenAlex
- W3000840260
- Cited by
- 265
- References
- 136
- Citations by year
- 2026: 39 · 2025: 63 · 2024: 69 · 2023: 29 · 2022: 37 · 2021: 26 · 2020: 2
- FWCI
- 25.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Death Anxiety and Social Exclusion
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- social exclusion, ostracism, mood, chatbot intervention, emotional support, buffer hypothesis, mental health
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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