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


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          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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          G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
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            This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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                Author and article information

                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                23 January 2020
                : 10
                : 3061
                [1] 1Department of Experimental Psychology, University College London , London, United Kingdom
                [2] 2Institute for Creative Technologies, University of Southern California , Los Angeles, CA, United States
                Author notes

                Edited by: Mario Weick, University of Kent, United Kingdom

                Reviewed by: Jeffrey M. Girard, Carnegie Mellon University, United States; Guillermo B. Willis, University of Granada, Spain

                *Correspondence: Eva G. Krumhuber, e.krumhuber@ 123456ucl.ac.uk

                This article was submitted to Personality and Social Psychology, a section of the journal Frontiers in Psychology

                Copyright © 2020 de Gennaro, Krumhuber and Lucas.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 04 September 2019
                : 26 December 2019
                Page count
                Figures: 2, Tables: 1, Equations: 0, References: 140, Pages: 14, Words: 0
                Original Research

                Clinical Psychology & Psychiatry
                social exclusion,empathy,mood,chatbot,virtual human
                Clinical Psychology & Psychiatry
                social exclusion, empathy, mood, chatbot, virtual human


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