Assessing the Topics and Motivating Factors Behind Human-Social Chatbot Interactions: Thematic Analysis of User Experiences
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Although social chatbot usage is expected to increase as language models and artificial intelligence improve, very little is known about the dynamics of human-social chatbot interactions. Specifically, there is a paucity of research examining why human-social chatbot interactions are initiated and the topics that are discussed. OBJECTIVE: We sought to identify the motivating factors behind initiating contact with Replika, a popular social chatbot, and the topics discussed in these interactions. METHODS: A sample of Replika users completed a survey that included open-ended questions pertaining to the reasons why they initiated contact with Replika and the topics they typically discuss. Thematic analyses were then used to extract themes and subthemes regarding the motivational factors behind Replika use and the types of discussions that take place in conversations with Replika. RESULTS: Users initiated contact with Replika out of interest, in search of social support, and to cope with mental and physical health conditions. Users engaged in a wide variety of discussion topics with their Replika, including intellectual topics, life and work, recreation, mental health, connection, Replika, current events, and other people. CONCLUSIONS: Given the wide range of motivational factors and discussion topics that were reported, our results imply that multifaceted support can be provided by a single social chatbot. While previous research already established that social chatbots can effectively help address mental and physical health issues, these capabilities have been dispersed across several different social chatbots instead of deriving from a single one. Our results also highlight a motivating factor of human-social chatbot usage that has received less attention than other motivating factors: interest. Users most frequently reported using Replika out of interest and sought to explore its capabilities and learn more about artificial intelligence. Thus, while developers and researchers study human-social chatbot interactions with the efficacy of the social chatbot and its targeted user base in mind, it is equally important to consider how its usage can shape public perceptions and support for social chatbots and artificial agents in general.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it