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Cited by 265Death Anxiety and Social ExclusionSocial Robot Interaction and HRIDigital Mental Health Interventions

The DOI matches its Crossref record; 132 of the 136 references it cites were found in a registry or an index.

Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood

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de Gennaro, Mauro, Krumhuber, Eva G., Lucas, Gale·2020·Frontiers in Psychology
DOI 10.3389/fpsyg.2019.03061PMC PMC6989433
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Abstract

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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  1. Bales, 1966, Size of group as a factor in the interaction profile, Small Groups: Studies in Social Interaction, 495
  2. Burri, 2018, Improving User Trust Towards Conversational Chatbot Interfaces With Voice Output (Dissertation).
  3. DeVault, 2014, SimInterviewer: a virtual human interviewer for healthcare decision support, Proceedings of the 13th Conference on Autonomous Agents and Multi-Agent Systems
  4. Dunbar, 1998, Grooming, Gossip, and the Evolution of Language.
  5. 2019, General Best Practices - Messenger Platform.
  6. Faulkner, 1997, The “silent treatment”: Its incidence and impact, Proceedings of the 69th Annual Midwestern Psychological Association
  7. Gardner, 2005, Social Snacking and Shielding: Using social symbols, selves, and surrogates in the service of belonging needs, The Social Outcast: Ostracism, Social Exclusion, Rejection, and Bullying
  8. Lim, 2017, Predicting Outcomes in Online Chatbot-Mediated Therapy.
  9. Lucas, 2010, Wanting connection, fearing rejection: the paradox of loneliness., Human, 17, 37
  10. Lucas, Getting to know each other: the role of social dialogue in recovery from errors in social robots, Proceedings of the Conference on Human-Robot Interaction
  11. Pickett, 2005, The social monitoring system: enhanced sensitivity to social cues as an adaptive response to social exclusion, The social Outcast: Ostracism, Social Exclusion, Rejection, and Bullying, 213
  12. Williams, 2005, The Social Outcast.
  13. Zumstein, 2017, Chatbots: an interactive technology for personalized communication, transactions and services., IADIS Int. J. Internet, 15, 96

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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.
ORCID
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2020-01-23
Volume
10
Pages
3061-3061
ISSN
1664-1078 (ISSN-L)
DOI
10.3389/fpsyg.2019.03061
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
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

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