Large language models (LLMs) are increasingly integrated into daily life through conversational interfaces, processing user data via natural language inputs and exhibiting advanced reasoning capabilities, which raises new concerns about user control over privacy. While much research has focused on potential privacy risks, less attention has been paid to the data control mechanisms these platforms provide. This study examines six conversational LLM platforms, analyzing how they define and implement features for users to access, edit, delete, and share data. Our analysis reveals an emerging paradigm of data control in conversational LLM platforms, where user data is generated and derived through interaction itself, natural language enables flexible yet often ambiguous control, and multi-user interactions with shared data raise questions of co-ownership and governance. Based on these findings, we offer practical insights for platform developers, policymakers, and researchers to design more effective and usable privacy controls in LLM-powered conversational interactions.

Personally identifiable information (PII) is a fundamental concept in privacy research and regulations. Understanding users' perspectives on PII is critical, as their understanding of PII can significantly affect their privacy decisions and practices. While much research has explored users’ privacy perceptions and disclosure preferences regarding PII, less attention has been focused on how users internally define and conceptualize PII. In this study, we conducted interviews with 32 participants to investigate their conceptualization and understanding of PII, using period and fertility tracking apps as the context. Our findings reveal how users perceive the processes and contexts through which personal information, by becoming identifiable, transitions into PII, as well as concerns about data sharing and misuse in these apps. We conclude by advocating for addressing the misalignment between users' perceptions of PII and the regulatory protections and privacy designs surrounding it.

In this study, we explore the effectiveness of persuasive messages endorsing the adoption of a privacy protection technology (IoT Inspector) tailored to individuals’ regulatory focus (promotion or prevention). We explore if and how regulatory fit (i.e., tuning the goal-pursuit mechanism to individuals’ internal regulatory focus) can increase persuasion and adoption. We conducted a between-subject experiment (N = 236) presenting participants with the IoT Inspector in gain ("Privacy Enhancing Technology"—PET) or loss ("Privacy Preserving Technology"—PPT) framing. Results show that the effect of regulatory fit on adoption is mediated by trust and privacy calculus processes: prevention-focused users who read the PPT message trust the tool more. Furthermore, privacy calculus favors using the tool when promotion-focused individuals read the PET message. We discuss the contribution of understanding the cognitive mechanisms behind regulatory fit in privacy decision-making to support privacy protection.

Multiplayer online games seek to address toxic behaviors such as trolling and griefing through behavior moderation, where penalties such as chat restriction or account suspension are issued against toxic players in the hope that punishments create a teachable moment for punished players to reflect and improve future behavior. While punishments impact player experience (PX) in profound ways, little is known regarding how players experience behavior moderation. In this study, we conducted a survey of 291 players to understand their experiences with punishments in online multiplayer games. Through several statistical analyses, we found that moderation explanation plays a critical role in improving players’ perceived transparency and fairness of moderation; and these perceptions significantly affect what players do after punishments. We discuss moderation experience as an important facet of PX, bridge the game and moderation literature, and provide design implications for behavior moderation in multiplayer online games.

We present an exploration of cultural norms surrounding online disclosure of information about one's interpersonal relationships (such as information about family members, colleagues, friends, or lovers) on Twitter. The literature identifies the cultural dimension of individualism versus collectivism as being a major determinant of offline communication differences in terms of emotion, topic, and content disclosed. We decided to study whether such differences also occur online in context of Twitter when comparing tweets posted in an individualistic (U.S.) versus a collectivist (India) society. We collected more than 2 million tweets posted in the U.S. and India over a 3 month period which contain interpersonal relationship keywords. A card-sort study was used to develop this culturally-sensitive saturated taxonomy of keywords that represent interpersonal relationships (e.g., ma, mom, mother). Then we developed a high-accuracy interpersonal disclosure detector based on dependency-parsing (F1-score: 86%) to identify when the words refer to a personal relationship of the poster (e.g., "my mom" as opposed to "a mom"). This allowed us to identify the 400K+ tweets in our data set which actually disclose information about the poster's interpersonal relationships. We used a mixed methods approach to analyze these tweets (e.g., comparing the amount of joy expressed about one's family) and found differences in emotion, topic, and content disclosed between tweets from the U.S. versus India. Our analysis also reveals how a combination of qualitative and quantitative methods are needed to uncover these differences; Using just one or the other can be misleading. This study extends the prior literature on Multi-Party Privacy and provides guidance for researchers and designers of culturally-sensitive systems.

Live streaming has become a popular activity world-wide that has warranted research attention on its privacy related issues. For instance, bystanders' privacy, or the privacy of third-parties captured by streamers, has been increasingly studied as live streaming has become almost ubiquitous in both public and private spaces in many countries. While prior work has studied bystanders' privacy concerns, a gap exists in understanding how streamers consider bystanders' privacy and the steps they take (or do not take) to preserve it. Understanding streamers' considerations towards bystanders' privacy is vital because streamers are the ones who have direct control over whether and how bystanders' information is disclosed. To address this gap, we conducted an interview study with 25 Chinese streamers to understand their considerations and practices regarding bystanders' privacy in live streaming. We found that streamers cared about bystanders' privacy and evaluated possible privacy violations to bystanders from several perspectives. To protect bystanders from privacy violations, streamers primarily relied on technical, behavioral, and collaborative strategies. Our results also indicated that current streaming platforms lacked features that helped streamers seamlessly manage bystanders' privacy and involved bystanders into their privacy decision-making. Applying the theoretical lens of collective privacy management, we discuss implications for the design of live streaming systems to support streamers in protecting bystanders' privacy.

Chatbot-based symptom checker (CSC) apps have become increasingly popular in healthcare. These apps engage users in human-like conversations and offer possible medical diagnoses. The conversational design of these apps can significantly impact user perceptions and experiences, and may influence medical decisions users make and the medical care they receive. However, the effects of the conversational design of CSCs remain understudied, and there is a need to investigate and enhance users’ interactions with CSCs. In this article, we conducted a two-stage exploratory study using a human-centered design methodology. We first conducted a qualitative interview study to identify key user needs in engaging with CSCs. We then performed an experimental study to investigate potential CSC conversational design solutions based on the results from the interview study. We identified that emotional support, explanations of medical information, and efficiency were important factors for users in their interactions with CSCs. We also demonstrated that emotional support and explanations could affect user perceptions and experiences, and they are context-dependent. Based on these findings, we offer design implications for CSC conversations to improve the user experience and health-related decision-making.

We conducted a user study with 380 Android users, profiling them according to two key privacy behaviors: the number of apps installed and the Dangerous permissions granted to those apps. We identified four unique privacy profiles: 1) Privacy Balancers (49.74% of participants), 2) Permission Limiters (28.68%), 3) App Limiters (14.74%), and 4) the Privacy Unconcerned (6.84%). App and Permission Limiters were significantly more concerned about perceived surveillance than Privacy Balancers and the Privacy Unconcerned. App Limiters had the lowest number of apps installed on their devices with the lowest intention of using apps and sharing information with them, compared to Permission Limiters who had the highest number of apps installed and reported higher intention to share information with apps. The four profiles reflect the differing privacy management strategies, perceptions, and intentions of Android users that go beyond the binary decision to share or withhold information via mobile apps.

Many social network sites (SNSs) have become available around the world and users’ online social networks increasingly include contacts from different cultures. However, there is lack of investigation into the concrete cultural differences in the effects of contextual factors and privacy concerns on users’ privacy decisions on social network sites (SNSs). The goal of this paper is to understand how contextual factors and privacy concerns cast different impact on privacy decisions, such as friend request decisions, information disclosure and perceived risk, in different countries. We performed a quantitative study through a large-scale online survey across the US, Korea and China to model the relationships between contextual factors, privacy concerns and privacy decisions. We find that the contextual influence and focus of privacy concerns vary between the individualistic and collectivistic countries in our sample. We suggest that multinational SNS service providers should consider different contextual factors and focus of privacy concerns in different countries and customise privacy designs and friend recommendation algorithms in SNSs in different countries.

Live streaming is a popular synchronous social media platform that allows users to disclose information to vast audience in real time. It has been increasingly studied in recent years for its unique functions of disseminating user-generated content, enriching streamers' self-presentation, curating online social interactions and fostering online communities. However, little research has been done to explore the privacy issues in live streaming. In the present paper, we aim to understand streamers' privacy concerns and strategies in their information disclosure on live streaming. From an interview study with 20 streamers, we found that they expressed concerns and carefully managed their information disclosure based on whether the disclosure would enhance or weaken their attractiveness to the audience and whether it would disturb their interpersonal boundary with the audience. They adopted various technical and behavioral privacy management strategies to cope with their concerns, but encountered a series of usability and cognitive burdens. Based on the findings, we present design implications to improve the privacy management on live streaming.

This paper addresses inconsistencies that exist in the measurement instruments HCI researchers use in cross-cultural studies. We study some commonly used measurement instruments that capture cultural dimensions at an individual level and conduct "measurement invariance tests," which test whether the questions comprising a construct have similar characteristics across different groups (e.g., countries). We find that these cultural dimensions are, to some extent, non-invariant, making statistical comparisons between countries problematic. Furthermore, we study the (non)invariance of the causal relationship between these cultural dimensions and privacy-related constructs, e.g., privacy concern and the amount of information users share on social media. Our results suggest that in several instances, these cultural dimensions have a different effect on privacy-related constructs per country. This severely reduces their usefulness for developing cross-cultural arguments in cross-country studies. We discuss the value of conducting measurement and causal non-invariance tests and urge scholars to develop more robust means of measuring culture.

2020's COVID-19 crisis has given rise to ubiquitous emergency remote learning (ERL). Guardians, mostly parents, have had to help their children transition and adapt to this difficult learning context. Previous work on remote learning has explored guardian involvement in pre-planned and well-developed remote learning programs, such as established virtual schools. However, ERL lacks pre-planned procedures, policies, and resources. In this paper, we look at how teachers and guardians collaborated to manage the situation. We present an interview study of guardians and teachers of K-12 students in China and look at their collaboration during the COVID-19 ERL. We report how teachers and guardians collaborated to carry out techno-procedural, surveillance, and material work to make ERL possible for K-12 students. Lastly, we reflect on the challenges our participants faced and discuss design implications not only for remote learning during COVID-19 but also future emergency remote learning situations.

Privacy has been conceptualized as a multi-dimensional construct in prior research. However, most multi-dimensional conceptualizations were developed based on populations from western countries. It remains an open question whether the underlying dimensions of privacy stays consistent in non-western countries. Through a series of factor analyses on two survey datasets, we compare the dimensions of privacy concern, information disclosure, general disclosiveness, and privacy management strategies among social network users in the US, China and South Korea. We find significant cross-country differences in the dimensions of these privacy-related concepts, indicating that the fundamental understanding of these concepts varies substantially across these countries. We discuss possible explanations of these cross-country differences and make methodological suggestions for future work.

Friend request acceptance and information disclosure constitute 2 important privacy decisions for users to control the flow of their personal information in social network sites (SNSs). These decisions are greatly influenced by contextual characteristics of the request. However, the contextual influence may not be uniform among users with different levels of privacy concerns. In this study, we hypothesize that users with higher privacy concerns may consider contextual factors differently from those with lower privacy concerns. By conducting a scenario‐based survey study and structural equation modeling, we verify the interaction effects between privacy concerns and contextual factors. We additionally find that users' perceived risk towards the requester mediates the effect of context and privacy concerns. These results extend our understanding about the cognitive process behind privacy decision making in SNSs. The interaction effects suggest strategies for SNS providers to predict user's friend request acceptance and to customize context‐aware privacy decision support based on users' different privacy attitudes.

Streakiness refers to observed tendency towards consecutive appearances of particular patterns. In video games, streakiness is oftentimes inevitable, where a player keeps winning or losing for a short period. However, the phenomenon remains understudied in present online game research. How do players perceive streakiness? How does it impact player experience (PX)? How should streakiness be taken into consideration for the design of PX? In this paper, we address these questions through a qualitative study of player discussions about streakiness in League of Legends. We found that players developed various ways to describe a streak. Both winning and losing streaks negatively impacted PX. Players devised numerous strategies to manage streakiness, among which disengagement was a primary means. We analyze streakiness as a social construct through which players coped with complex game systems. We discuss design implications for managing streakiness in online games.

Privacy researchers have suggested various granular audience control techniques for users to manage the access to their disclosed information on social network sites. However, it is unclear how users adopt and utilize such techniques in daily use and how these techniques may impact social interactions with others over time. In this study, we examine users' experience during everyday use of granular audience control techniques in WeChat, one of the most popular social network applications in China. Through an interview study with 24 WeChat users, we find that users adjust their configurations and develop rationales for these configurations over time. They also perceive mixed impacts of WeChat's privacy settings on their information disclosure and social interactions, which brings new challenges to privacy design. We discuss the implications of these findings and make suggestions for the future design of privacy settings in SNSs.

Live streaming is a new media format that simultaneously records and broadcasts in real time, in multiple channels including video, audio, and text. A new application area of live streaming is in the video game community, where players stream their gameplay. Since most of the streamed games are team-based, one's live streaming may involve other players' gameplay and disclosure. In the present paper, we aim to understand the attitudes and strategies of players who have been streamed by others in video game live streaming. From an interview study with 20 World of Warcraft (WoW) players, we found that participants had concerns about being streamed due to different factors, and adopted individual and collaborative strategies to cope with their concerns. We discuss privacy challenges in live streaming and present design implications for live streaming tools to improve privacy management.

If one wants to study privacy from an intercultural perspective, one must first validate whether there are any cultural variations in the concept of “privacy” itself. This study systematically examines cultural differences in collective privacy management strategies, and highlights methodological precautions that must be taken in quantitative intercultural privacy research. Using survey data of 498 Facebook users from the US, Singapore, and South Korea, we test the validity and cultural invariance of the measurement model and predictive model associated with collective privacy management. The results show that the measurement model is only partially culturally invariant, indicating that social media users in different countries interpret the same instruments in different ways. Also, cross-national comparisons of the structural model show that causal pathways from collective privacy management strategies to privacy-related outcomes vary significantly across countries. The findings suggest significant cultural variations in privacy management practices, both with regard to the conceptualization of its theoretical constructs, and with respect to causal pathways.

Abstract The influence of cultural background on people’s privacy decisions is widely recognized. However, a cross-cultural approach to predicting privacy decisions is still lacking. Our paper presents a first integrated cross-cultural privacy prediction model that merges cultural, demographic, attitudinal and contextual prediction. The model applies supervised machine learning to users’ decisions on the collection of their personal data, collected from a large-scale quantitative study in eight different countries. We find that adding culture-related predictors (i.e. country of residence, language, Hofstede’s cultural dimensions) to demographic, attitudinal and contextual predictors in the model can improve the prediction accuracy. Hofstede’s variables - particularly individualism and indulgence - outperform country and language. We further apply generalized linear mixed-effect regression to explore possible interactions between culture and other predictors. We find indeed that the impact of contextual and attitudinal predictors varies between different cultures. The implications of such models in developing privacy-enabling technologies are discussed.