MM
M.H.C. Meyer
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
1 records found
1
Recommendations, Textual Anthropomorphic Cues, and Self-Disclosure in Mental-Health Conversations with ChatGPT
A Turn-Level Analysis Across Conversation Positions
Context and Research Aim
Generative conversational AI is increasingly used for personal and mental -healthrelated conversations in which users may share sensitive experiences, thoughts, and feelings (Karnam et al., 2026; Siddals et al., 2024). In these conversations, selfdisclosure occurs as part of an ongoing message-by-message exchange between the user and ChatGPT. However, existing research provides limited insight into how self - disclosure unfolds at the level of individual exchanges in naturally occurring conversations, both in relation to characteristics of the preceding Cha tGPT response and to where the exchange occurs within the conversation (Greilich et al., 2025; Karnam et al., 2026; Li et al., 2023; Skjuve et al., 2023). This thesis therefore examines how ChatGPT recommendations, human-like wording, and where an exchange occurs within a conversation relate to subsequent user self-disclosure in mental-health conversations.
Approach
To investigate these message-to-message relationships, the study analysed 69
donated English-language mental-health conversations comprising 2,639 ChatGPTuser exchanges. It focused on two characteristics of ChatGPT responses,
recommendations and human-like wording, and examined how they related to selfdisclosure in the user response that immediately followed. The exchanges were qualitatively coded to identify these characteristics and subsequent self -disclosure. The resulting codes were then analysed quantitatively, while also considering where each exchange occurred within the conversation.
Key Findings
Recommendations were not consistently related to subsequent self -disclosure, including when users continued in the direction suggested by the recommendation. Human-like wording showed the same pattern, with no consistent relationship to subsequent self-disclosure. The clearest pattern concerned where an exchange occurred within the conversation: the sensitivity and amount of personal information varied across conversation position, with several of the observed differences concentrated in the earlier part of conversations.
Implications
For product teams, the findings suggest evaluating recommendations, human -like wording, and self-disclosure as separate aspects of the interaction. When a
conversational feature is used for a particular purpose, teams should evaluate whether it supports that purpose while assessing the personal information users disclose separately. Because users tended to share more, and more sensitive, information near the beginning of conversations, privacy checks should pay particular attention to these early exchanges. This helps teams assess whether a conversational feature is useful while also considering whether the amount an sensitivity of the information users share match what is actually needed for the purpose of the interaction ...
Generative conversational AI is increasingly used for personal and mental -healthrelated conversations in which users may share sensitive experiences, thoughts, and feelings (Karnam et al., 2026; Siddals et al., 2024). In these conversations, selfdisclosure occurs as part of an ongoing message-by-message exchange between the user and ChatGPT. However, existing research provides limited insight into how self - disclosure unfolds at the level of individual exchanges in naturally occurring conversations, both in relation to characteristics of the preceding Cha tGPT response and to where the exchange occurs within the conversation (Greilich et al., 2025; Karnam et al., 2026; Li et al., 2023; Skjuve et al., 2023). This thesis therefore examines how ChatGPT recommendations, human-like wording, and where an exchange occurs within a conversation relate to subsequent user self-disclosure in mental-health conversations.
Approach
To investigate these message-to-message relationships, the study analysed 69
donated English-language mental-health conversations comprising 2,639 ChatGPTuser exchanges. It focused on two characteristics of ChatGPT responses,
recommendations and human-like wording, and examined how they related to selfdisclosure in the user response that immediately followed. The exchanges were qualitatively coded to identify these characteristics and subsequent self -disclosure. The resulting codes were then analysed quantitatively, while also considering where each exchange occurred within the conversation.
Key Findings
Recommendations were not consistently related to subsequent self -disclosure, including when users continued in the direction suggested by the recommendation. Human-like wording showed the same pattern, with no consistent relationship to subsequent self-disclosure. The clearest pattern concerned where an exchange occurred within the conversation: the sensitivity and amount of personal information varied across conversation position, with several of the observed differences concentrated in the earlier part of conversations.
Implications
For product teams, the findings suggest evaluating recommendations, human -like wording, and self-disclosure as separate aspects of the interaction. When a
conversational feature is used for a particular purpose, teams should evaluate whether it supports that purpose while assessing the personal information users disclose separately. Because users tended to share more, and more sensitive, information near the beginning of conversations, privacy checks should pay particular attention to these early exchanges. This helps teams assess whether a conversational feature is useful while also considering whether the amount an sensitivity of the information users share match what is actually needed for the purpose of the interaction ...
Context and Research Aim
Generative conversational AI is increasingly used for personal and mental -healthrelated conversations in which users may share sensitive experiences, thoughts, and feelings (Karnam et al., 2026; Siddals et al., 2024). In these conversations, selfdisclosure occurs as part of an ongoing message-by-message exchange between the user and ChatGPT. However, existing research provides limited insight into how self - disclosure unfolds at the level of individual exchanges in naturally occurring conversations, both in relation to characteristics of the preceding Cha tGPT response and to where the exchange occurs within the conversation (Greilich et al., 2025; Karnam et al., 2026; Li et al., 2023; Skjuve et al., 2023). This thesis therefore examines how ChatGPT recommendations, human-like wording, and where an exchange occurs within a conversation relate to subsequent user self-disclosure in mental-health conversations.
Approach
To investigate these message-to-message relationships, the study analysed 69
donated English-language mental-health conversations comprising 2,639 ChatGPTuser exchanges. It focused on two characteristics of ChatGPT responses,
recommendations and human-like wording, and examined how they related to selfdisclosure in the user response that immediately followed. The exchanges were qualitatively coded to identify these characteristics and subsequent self -disclosure. The resulting codes were then analysed quantitatively, while also considering where each exchange occurred within the conversation.
Key Findings
Recommendations were not consistently related to subsequent self -disclosure, including when users continued in the direction suggested by the recommendation. Human-like wording showed the same pattern, with no consistent relationship to subsequent self-disclosure. The clearest pattern concerned where an exchange occurred within the conversation: the sensitivity and amount of personal information varied across conversation position, with several of the observed differences concentrated in the earlier part of conversations.
Implications
For product teams, the findings suggest evaluating recommendations, human -like wording, and self-disclosure as separate aspects of the interaction. When a
conversational feature is used for a particular purpose, teams should evaluate whether it supports that purpose while assessing the personal information users disclose separately. Because users tended to share more, and more sensitive, information near the beginning of conversations, privacy checks should pay particular attention to these early exchanges. This helps teams assess whether a conversational feature is useful while also considering whether the amount an sensitivity of the information users share match what is actually needed for the purpose of the interaction
Generative conversational AI is increasingly used for personal and mental -healthrelated conversations in which users may share sensitive experiences, thoughts, and feelings (Karnam et al., 2026; Siddals et al., 2024). In these conversations, selfdisclosure occurs as part of an ongoing message-by-message exchange between the user and ChatGPT. However, existing research provides limited insight into how self - disclosure unfolds at the level of individual exchanges in naturally occurring conversations, both in relation to characteristics of the preceding Cha tGPT response and to where the exchange occurs within the conversation (Greilich et al., 2025; Karnam et al., 2026; Li et al., 2023; Skjuve et al., 2023). This thesis therefore examines how ChatGPT recommendations, human-like wording, and where an exchange occurs within a conversation relate to subsequent user self-disclosure in mental-health conversations.
Approach
To investigate these message-to-message relationships, the study analysed 69
donated English-language mental-health conversations comprising 2,639 ChatGPTuser exchanges. It focused on two characteristics of ChatGPT responses,
recommendations and human-like wording, and examined how they related to selfdisclosure in the user response that immediately followed. The exchanges were qualitatively coded to identify these characteristics and subsequent self -disclosure. The resulting codes were then analysed quantitatively, while also considering where each exchange occurred within the conversation.
Key Findings
Recommendations were not consistently related to subsequent self -disclosure, including when users continued in the direction suggested by the recommendation. Human-like wording showed the same pattern, with no consistent relationship to subsequent self-disclosure. The clearest pattern concerned where an exchange occurred within the conversation: the sensitivity and amount of personal information varied across conversation position, with several of the observed differences concentrated in the earlier part of conversations.
Implications
For product teams, the findings suggest evaluating recommendations, human -like wording, and self-disclosure as separate aspects of the interaction. When a
conversational feature is used for a particular purpose, teams should evaluate whether it supports that purpose while assessing the personal information users disclose separately. Because users tended to share more, and more sensitive, information near the beginning of conversations, privacy checks should pay particular attention to these early exchanges. This helps teams assess whether a conversational feature is useful while also considering whether the amount an sensitivity of the information users share match what is actually needed for the purpose of the interaction