When the Social Becomes Non-Human: Young People's Perception of Social Support in Chatbots

Brandtzæg, Petter Bae; Skjuve, Marita; Dysthe, Kim Kristoffer; Følstad, Asbjørn

doi:10.1145/3411764.3445318

Cited by 67 publications

(54 citation statements)

References 71 publications

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“…First, the data in this study are limited to users who accessed and used a particular chatbot. Given that the primary user group of the chatbot service studied is young adults [ 4 ], our findings may not be generalizable to other populations and age groups. Our study also focused on a particular social chatbot, and its findings might not be generalizable to other types of conversational agents.…”

Section: Discussionmentioning

confidence: 99%

“…Chatbots are an emerging digital platform that has received considerable attention from public health initiatives during the novel coronavirus (COVID-19) pandemic [ 3 ]. Chatbots are systems that interact with users via text or voice and use natural language [ 4 ]. They have been widely used for amusement (eg, small talk) and business (eg, customer service and sales) [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Chatbots have several advantages over human interaction partners or other online services. For example, chatbots operate at a low cost and are available around the clock to handle user queries [ 4 , 8 , 9 ]. Furthermore, they can provide more concise and less overwhelming information than the lengthy list of results offered by social media or search engines [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

et al. 2023

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Background Chatbots have become a promising tool to support public health initiatives. Despite their potential, little research has examined how individuals interacted with chatbots during the COVID-19 pandemic. Understanding user-chatbot interactions is crucial for developing services that can respond to people’s needs during a global health emergency. Objective This study examined the COVID-19 pandemic–related topics online users discussed with a commercially available social chatbot and compared the sentiment expressed by users from 5 culturally different countries. Methods We analyzed 19,782 conversation utterances related to COVID-19 covering 5 countries (the United States, the United Kingdom, Canada, Malaysia, and the Philippines) between 2020 and 2021, from SimSimi, one of the world’s largest open-domain social chatbots. We identified chat topics using natural language processing methods and analyzed their emotional sentiments. Additionally, we compared the topic and sentiment variations in the COVID-19–related chats across countries. Results Our analysis identified 18 emerging topics, which could be categorized into the following 5 overarching themes: “Questions on COVID-19 asked to the chatbot” (30.6%), “Preventive behaviors” (25.3%), “Outbreak of COVID-19” (16.4%), “Physical and psychological impact of COVID-19” (16.0%), and “People and life in the pandemic” (11.7%). Our data indicated that people considered chatbots as a source of information about the pandemic, for example, by asking health-related questions. Users turned to SimSimi for conversation and emotional messages when offline social interactions became limited during the lockdown period. Users were more likely to express negative sentiments when conversing about topics related to masks, lockdowns, case counts, and their worries about the pandemic. In contrast, small talk with the chatbot was largely accompanied by positive sentiment. We also found cultural differences, with negative words being used more often by users in the United States than by those in Asia when talking about COVID-19. Conclusions Based on the analysis of user-chatbot interactions on a live platform, this work provides insights into people’s informational and emotional needs during a global health crisis. Users sought health-related information and shared emotional messages with the chatbot, indicating the potential use of chatbots to provide accurate health information and emotional support. Future research can look into different support strategies that align with the direction of public health policy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

et al. 2023

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“…This could be done using clinical interviews supplementing self-reporting data for the same type of empirical development. Previous research has showed that adolescents appreciate support from conversational agents such as Woebot [ 63 ]. However, the field of adolescent mental health is complex and involves different life areas such as school, social life, parents, and family.…”

Section: Discussionmentioning

confidence: 99%

Analyzing User-Generated Web-Based Posts of Adolescents’ Emotional, Behavioral, and Symptom Responses to Beliefs About Depression: Qualitative Thematic Analysis

Dysthe¹,

Røssberg²,

Brandtzæg³

et al. 2023

J Med Internet Res

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Background Depression is common during adolescence. Early intervention can prevent it from developing into more progressive mental disorders. Combining information technology and clinical psychoeducation is a promising way to intervene at an earlier stage. However, data-driven research on the cognitive response to health information targeting adolescents with symptoms of depression is lacking. Objective This study aimed to fill this knowledge gap through a new understanding of adolescents’ cognitive response to health information about depression. This knowledge can help to develop population-specific information technology, such as chatbots, in addition to clinical therapeutic tools for use in general practice. Methods The data set consists of 1870 depression-related questions posted by adolescents on a public web-based information service. Most of the posts contain descriptions of events that lead to depression. On a sample of 100 posts, we conducted a qualitative thematic analysis based on cognitive behavioral theory investigating behavioral, emotional, and symptom responses to beliefs associated with depression. Results Results were organized into four themes. (1) Hopelessness, appearing as a set of negative beliefs about the future, possibly results from erroneous beliefs about the causal link between risk factors and the course of depression. We found beliefs about establishing a sturdy therapy alliance as a responsibility resting on the patient. (2) Therapy hesitancy seemed to be associated with negative beliefs about therapy prognosis and doubts about confidentiality. (3) Social shame appeared as a consequence of impaired daily function when the cause is not acknowledged. (4) Failing to attain social interaction appeared to be associated with a negative symptom response. In contrast, actively obtaining social support reduces symptoms and suicidal thoughts. Conclusions These results could be used to meet the clinical aims stated by earlier psychoeducation development, such as instilling hope through direct reattribution of beliefs about the future; challenging causal attributions, thereby lowering therapy hesitancy; reducing shame through the mechanisms of externalization by providing a tentative diagnosis despite the risk of stigmatizing; and providing initial symptom relief by giving advice on how to open up and reveal themselves to friends and family and balance the message of self-management to fit coping capabilities. An active counseling style advises the patient to approach the social environment, demonstrating an attitude toward self-action.

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“…Bae Brandtzæg et al. (2021) designed a chatbot to support young people with sex, school, bullying, feelings, love, and divorce issues. They found that users perceived social support from the chatbot and that it was easier to self‐disclose to a chatbot than to a human because chatbots provide anonymity and privacy.…”

Section: Introductionmentioning

confidence: 99%

Development of a chatbot for depression: adolescent perceptions and recommendations

Dosovitsky

Bunge

2022

Child Adoles Ment Health

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Background: Chatbots are a relatively new technology that has shown promising outcomes for mental health symptoms in adults; however, few studies have been done with adolescents or reported adolescent user experiences and recommendations for chatbot development. Methods: Twenty three participants ages 13-18 (M age = 14.96) engaged in user testing of a chatbot developed to psychoeducate adolescents on depression, teach behavioral activation, and change negative thoughts. Thematic analysis was conducted of participants' responses to user experience questions, impressions, and recommendations. Results: Over half (56.5%) of the sample completed the full intervention and provided user experience feedback online. The average NPS score was 6.04 (SD = 2.18), and 64.3% (n = 9) said they would use the chatbot in the future. Of all user experience responses, 54.5% were positive. The most common impressions were related to symptom improvement (61.1%) and availability (52.8%) The most frequent recommendations were related to solving technical problems (66%). Conclusions: Chatbots for mental health are acceptable to some adolescents, a population that tends to be reluctant to engage with traditional mental health services. Most participants reported positive experiences with the chatbot, believing that it could help with symptom improvement and is highly available. Adolescents highlighted some technical and stylistic problems that developers should consider. More pilot and user testing is needed to develop mental health chatbots that are appealing and relevant to adolescents. Key practitioner message• Chatbots are a relatively new technology; however, few studies have been done with adolescents.• Most adolescents perceived the chatbot positively and believed that it could help with symptom improvement and that it is highly available. • Chatbots for mental health are acceptable to some adolescents, a population that tends to be reluctant to engage with traditional mental health services.

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When the Social Becomes Non-Human: Young People's Perception of Social Support in Chatbots

Cited by 67 publications

References 71 publications

User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

Analyzing User-Generated Web-Based Posts of Adolescents’ Emotional, Behavioral, and Symptom Responses to Beliefs About Depression: Qualitative Thematic Analysis

Development of a chatbot for depression: adolescent perceptions and recommendations

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