## 1 Introduction
As a result, attempts to direct or influence this behavior in chatbot interactions should focus on cues triggering automatic thinking (i.e., peripheral) rather than intentional (i.e., central) thinking [14; 28]. Information disclosure is also known to be malleable. This means that certain aspects of the online environment can be manipulated to influence privacy behavior [14; 29]. Therefore, there is room for interventions that raise awareness of the risks and allow for more cautious disclosure of information. Persuasion Theories [30; 31] may be considered when designing information disclosure nudges of various types, such as sensitivity signals and social proof nudges. Sensitivity signals are simple labels that describe the level of sensitivity (e.g., low or high) of the various questions being asked by a chatbot. Making users conscious of the sensitivity of the information they are about to share may influence their subsequent disclosure behaviors [31]. Social proof nudges are indications of how popular something is; in the context of information disclosure, a social proof nudge would suggest to what extent a chatbot question had been answered by other users. This validation of sorts may influence users into mimicking the same behavior as their fellows [30]. To the best of the authors' knowledge, nudges pertaining to information disclosure have been largely overlooked by extant privacy and chatbot research. Given the above-mentioned need for promoting users' informed decision-making about online information disclosures and the potential for nudges to achieve that outcome, this research intends to answer the following research question (RQ):  
**RQ 1**. : _How do different types of information disclosure nudges (here, sensitivity signal and social proof) and question sensitivity affect the level of users' behavioral information disclosure during chatbot interactions?_  
To understand how nudging influence manifests in users-chatbot interactions, the role of a user's emotional response to information disclosure nudges and the consequent information disclosure behaviors is also explored. The Affect Infusion Model (AIM) ex plains ways in which people's judgments are influenced by their emotional responses as they process information and their resulting actions [32]. According to the AIM, users may process information disclosure nudges heuristically, i.e., they may base their choice to reveal specific information on their emotional state evoked by the available cues in the interaction context. Limited research on user emotional responses during chatbot exchanges exists, including studies on how generating positive versus negative emotional responses from users, leads to more or less conversational breakdowns, and exploring the role of empathy in providing supportive medical information through chatbots [33; 34; 35]. Nonetheless, emotional responses in the context of privacy notices and/or information disclosure behaviors have not been explored yet. Therefore, this research also aims to answer the following RQ:  
**RQ 2**. : _Does user emotional response mediate the effects of question sensitivity and information disclosure nudge type on their disclosure behavior?_  
This research is important not only for advancing knowledge in user experience and informing regulatory policies, but also for the larger ethical discussions surrounding AI (e.g., the lack of regulatory frameworks, the rapid development of technology, the significant risks associated with online information disclosure, and the high return potential of AI) [36]. Chatbots powered by AI are also being studied, and their usage, as well as power, is being questioned, especially surrounding the data they capture and use [37]. Data ethics is a branch of ethics that seeks to evaluate ethical issues brought about by data practices [38]. Ethical questions happen throughout the data life cycle (i.e., collection, storage, processing, use, sharing, and archive) and every step represents a risk for the user [39].