AUTHOR=Dosovitsky Gilly , Bunge Eduardo L. TITLE=Bonding With Bot: User Feedback on a Chatbot for Social Isolation JOURNAL=Frontiers in Digital Health VOLUME=3 YEAR=2021 URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2021.735053 DOI=10.3389/fdgth.2021.735053 ISSN=2673-253X ABSTRACT=

Social isolation has affected people globally during the COVID-19 pandemic and had a major impact on older adult's well-being. Chatbot interventions may be a way to provide support to address loneliness and social isolation in older adults. The aims of the current study were to (1) understand the distribution of a chatbot's net promoter scores, (2) conduct a thematic analysis on qualitative elaborations to the net promoter scores, (3) understand the distribution of net promoter scores per theme, and (4) conduct a single word analysis to understand the frequency of words present in the qualitative feedback. A total of 7,099 adults and older adults consented to participate in a chatbot intervention on reducing social isolation and loneliness. The average net promoter score (NPS) was 8.67 out of 10. Qualitative feedback was provided by 766 (10.79%) participants which amounted to 898 total responses. Most themes were rated as positive (517), followed by neutral (311) and a minor portion as negative (70). The following five themes were found across the qualitative responses: positive outcome (277, 30.8%), user did not address question (262, 29.2%), bonding with the chatbot (240, 26.7%), negative technical aspects (70, 7.8%), and ambiguous outcome (49, 5.5%). Themes with a positive valence were found to be associated with a higher NPS. The word “help” and it's variations were found to be the most frequently used words, which is consistent with the thematic analysis. These results show that a chatbot for social isolation and loneliness was perceived positively by most participants. More specifically, users were likely to personify the chatbot (e.g., “Cause I feel like I have a new friend!”) and perceive positive personality features such as being non-judgmental, caring, and open to listen. A minor portion of the users reported dissatisfaction with chatting with a machine. Implications will be discussed.