Background: Artificial intelligence chatbot research has focused on technical advances in natural language processing and validating the effectiveness of human-machine conversations in specific settings. However, real-world chat data remain proprietary and unexplored despite their growing popularity, and new analyses of chatbot uses and their effects on mitigating negative moods are urgently needed. Objective: In this study, we investigated whether and how artificial intelligence chatbots facilitate the expression of user emotions, specifically sadness and depression. We also examined cultural differences in the expression of depressive moods among users in Western and Eastern countries. Methods: This study used SimSimi, a global open-domain social chatbot, to analyze 152,783 conversation utterances containing the terms “depress” and “sad” in 3 Western countries (Canada, the United Kingdom, and the United States) and 5 Eastern countries (Indonesia, India, Malaysia, the Philippines, and Thailand). Study 1 reports new findings on the cultural differences in how people talk about depression and sadness to chatbots based on Linguistic Inquiry and Word Count and n-gram analyses. In study 2, we classified chat conversations into predefined topics using semisupervised classification techniques to better understand the types of depressive moods prevalent in chats. We then identified the distinguishing features of chat-based depressive discourse data and the disparity between Eastern and Western users. Results: Our data revealed intriguing cultural differences. Chatbot users in Eastern countries indicated stronger emotions about depression than users in Western countries (positive: P<.001; negative: P=.01); for example, Eastern users used more words associated with sadness (P=.01). However, Western users were more likely to share vulnerable topics such as mental health (P<.001), and this group also had a greater tendency to discuss sensitive topics such as swear words (P<.001) and death (P<.001). In addition, when talking to chatbots, people expressed their depressive moods differently than on other platforms. Users were more open to expressing emotional vulnerability related to depressive or sad moods to chatbots (74,045/148,590, 49.83%) than on social media (149/1978, 7.53%). Chatbot conversations tended not to broach topics that require social support from others, such as seeking advice on daily life difficulties, unlike on social media. However, chatbot users acted in anticipation of conversational agents that exhibit active listening skills and foster a safe space where they can openly share emotional states such as sadness or depression. Conclusions: The findings highlight the potential of chatbot-assisted mental health support, emphasizing the importance of continued technical and policy-wise efforts to improve chatbot interactions for those in need of emotional assistance. Our data indicate the possibility of chatbots providing helpful information about depressive moods, especially for users who have difficulty communicating emotions to other humans.

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.

Conversational agents (CAs) offer new functionality and convenience. While their sales have been soaring, they have also rapidly become victims of verbal abuse by their users. Without proper handling of abusive usage, abusers’ actions can be reinforced and transferred to real life. This study investigates whether alternative response styles of empathy orientation and emotional expressivity of voice-activated virtual assistants influence users’ moral emotions found to reduce verbal aggression as well as whether they affect user perceptions of the agent’s capability. Ninety-eight participants were assigned to one of the three emotional expressivity conditions (no-facial expression, fixed-facial expression, varied-facial expression) and interacted with two alternative empathy orientation conditions (other-oriented, self-oriented) of agents. The experimental results show that, regardless of the emotional expressivity types, the agent’s empathy orientation has a significant effect on the moral emotions and agent capability perceptions. Overall, an agent that employed other-oriented empathy style elicited most positive responses from the users. However, the preference was not across the board, as about one-third of the participants showed preference to the self-oriented CA. Users valued agents’ verbal contents and vocal characteristics above their facial expressions. Based on the study findings, we draw several design guidelines and suggest avenues for future research.

Natural language processing is enabling machines to communicate with humans naturally, yet the dynamics of extended user-chatbot interactions remain much unexplored. This study characterizes the conversational styles, demographics, psychologies, and emotional tendencies of the most active users (i.e., top 1% by message count) of a commercial chatbot platform (SimSimi.com), whom we refer to as superusers. We analyzed the linguistic patterns and topics of 1,988,971 messages written by 1,994 superusers over a period of three years. We further surveyed 76 users to observe their emotional dispositions and perceptions towards the chatbot. We find that SimSimi superusers empathize and humanize the chatbot more than less active users, and they show a higher tendency to share personal and negative feelings. Our findings suggest that chatbots require new design considerations for users who are vulnerable due to their high anthropomorphism and openness toward machines. Our work also shows that chatbots should have functions to offer social support when necessary.

Background: While recent studies showed the potential of conversational agents (CAs) to help alleviate depressive moods, the dynamics of user-chatbot interactions in mental health support remain underexplored. Objective: We examine real-world conversations between users and chatbots on depression-related topics to identify patterns in how users seek help and how chatbots provide therapeutic support. We analyzed the responses of 8 commercial chatbots to user queries about depressive moods, examining whether they incorporated therapeutic communication techniques, such as empathy. Methods: Our method has 2 parts. First, we analyzed 13,700 utterances (6850 user queries and 6850 responses) about depressive moods from the commercial chatbot SimSimi, covering 5 English-speaking countries between 2016 and 2021. Using a human-annotated coding approach, we classified user queries into 5 groups based on Rickwood’s help-seeking model and classified chatbot responses into 8 therapeutic communication styles. Empathy was assessed as one of these styles, with responses coded as empathetic when they demonstrated emotional understanding, validation, and reflection. Next, we evaluated the responses of 3 voice assistants (Amazon’s Alexa, Google Assistant, and Apple’s Siri) and 5 chatbots (ChatGPT, Replika, Woebot, Wysa, and SimSimi) to user queries about depressive moods. Results: In study 1, we examined how SimSimi, a social chatbot trained to encourage users to share their emotions and build rapport, responded to user queries. The majority (3067/4073, 75.3%) indicated depressed feelings, and a smaller portion (168/4073, 4.1%) sought strategies to cope with depression. The chatbot’s responses were largely therapeutic ( 2417/3108, 77.7%), demonstrating empathy (902/3108, 29%), active listening (836/3108, 26.9%), and open-ended questions (679/3108, 21.8%). In study 2, we qualitatively compared response patterns across commercial CAs, revealing that Replika expressed empathy in more than 75% (28/36) of its responses, similar to SimSimi. In contrast, Alexa (15/17, 88.2%), Google Assistant (18/30, 60%), Siri (20/36, 55.6%), and ChatGPT (40/42, 95.2%) typically responded to depression-related queries with search results rather than offering specific solutions for depressive feelings. Mental health chatbots such as Woebot responded to users with clarification questions (97.3%). We also report instances where CAs failed to meet users’ help-seeking needs, instead giving irrelevant responses and ignoring emotional requests. Conclusions: Our findings reveal a mixed landscape in the emotional support provided by CAs. While some social chatbots delivered empathetic responses that fostered deeper user engagement, most commercial chatbots offered merely informative replies to users’ help-seeking inputs. Recognizing that users seek support from chatbots, we recommend equipping next-generation CAs with capabilities grounded in therapeutic communication, such as empathetic responses.