Abstract Online companies surveil users around the world for commercial purposes. We advance protection motivation theory (PMT), by categorizing user responses to surveillance as either active or passive (i.e., self-inhibition) privacy protection behavior, integrating privacy literacy overconfidence, and exploring privacy resignation as a moderator. Finally, we adopt a comparative lens by investigating protection behavior in four national contexts: Brazil, Germany, the United States, and Vietnam. Results of an online survey (Ntotal = 2,092) show that perceiving surveillance as privacy threat was not consistently associated with active protection behavior. Rather, self-inhibition was a more likely response to surveillance which may have detrimental consequences for both individuals and societies. Across countries, perceived effectiveness of active protection positively relates to both forms of protection while active protection does not prevent self-inhibition. Interestingly, privacy resignation may not always impede protection. In summary, not all aspects of PMT are generalizable when using a culturally diverse sample.
Abstract The integration of artificial intelligence (AI) into the intimate sphere of human relationships presents a profound challenge to traditional understanding of intimacy, necessitating a critical examination of this technology’s extension into a domain that has always been viewed as deeply human. Through in-depth semi-structured interviews (N = 23), this study examines how human–AI intimate relationships develop in Chinese society at the intersection of traditions and modernization. A three-pronged Context-Script-Cue (CSC) framework emerges, capturing how users navigate emotional investment alongside the artificial nature of AI. By empirically examining how users engage in human–AI intimate relationships, this study offers the new CSC framework for understanding the development of human–AI intimate relationships.
Abstract A conundrum of digital disconnection research is that individuals who report digitally disconnecting more often report poorer digital well-being. This article explores whether underlying differences in trait self-control might explain this conundrum. Study 1 (N = 1,315 adults) shows support for a Simpson’s paradox: Within persons, deliberate digital disconnection from the smartphone is linked to reduced smartphone ill-being, but between persons it links to increased smartphone ill-being. Negative between-person associations between trait self-control and both deliberate disconnection and smartphone ill-being suggest self-control as a confounder. Study 2 (N = 2,000 adults) explores this hypothesis further by contrasting more effortful, deliberated disconnection strategies with disengagement strategies reflecting situational goal-alignment. The findings show cognitive control associates positively with the use effortful disconnection strategies, and negatively with using less effortful disengagement strategies. Combined, the studies suggest digital disconnection takes more effort for some people than for others. This has implications for intervention design.
Abstract Previous research suggests that the ease (i.e., processing fluency) of generating future relational plans leads to perceptions of greater relational closeness and commitment. Can generative artificial intelligence (GenAI), when used to help users generate relational maintenance plans, increase fluency and thereby enhance users’ perceived closeness and commitment in friendships? Does the anthropomorphism of GenAI matter? Drawing upon prior work on processing fluency and extended cognition, we conducted a 2 (plan quantity: generating three vs. 10 plans) × 3 (agency: internal retrieval vs. anthropomorphic AI vs. nonanthropomorphic AI) online experiment (N = 1,397). After establishing the ease-of-retrieval effects on friendship closeness and commitment during internal retrieval, we found that both nonanthropomorphic and anthropomorphic AI (vs. internal retrieval) increased fluency, through which they further enhanced perceived friendship closeness and commitment. Despite taking a longer time to operate, the anthropomorphic AI induced a greater level of fluency than the nonanthropomorphic AI. We discuss theoretical implications for the ease-of-retrieval effect in interpersonal planning, AI-mediated communication, and human–machine communication.
This study investigates how cumulative content moderation shapes the longitudinal participation of distinct user groups within online communities. Utilizing a large-scale dataset from Reddit in 2022-comprising 530.5 million engagement records and 13.3 million moderation actions-we examined the interplay between moderation strategy (i.e., hard approaches vs. soft approaches), moderator identity (i.e., human vs. machine), and user heterogeneity (i.e., highly active vs. less active). Results indicate that moderation predominantly influences highly active users; for this group, an increase in the amount of silent removal significantly reduces subsequent engagement. Conversely, soft approaches (e.g., suggestion for users) bolster subsequent engagement, but only when executed by human moderators. Increased automated intervention in a community was associated with a decline in engagement among highly active members. These findings suggest that for community sustainability, the communicative mode and identity of the moderator are more critical than the moderation action itself. Online communities rely on rules and moderation to keep discussions safe and constructive. But it is unclear whether frequent moderation helps a group or makes people feel less free and less likely to join in. Using a large dataset from Reddit, we examined how different ways of moderating-and whether a human or a bot does it-change how much people participate. We found that people respond to moderation in different ways depending on how active they are. The highly active users who post and comment the most are the most sensitive to moderation actions. When their posts are silently removed without a reason, these core members post less often afterward. However, when human moderators provide helpful tips and suggestions for future posts, it encourages users to stay active. Moderation done by bots, in contrast, often makes the most active users participate less. These findings suggest that keeping a community lively is not just about enforcing rules. Instead of relying only on bots or silent removals, communities should use human-centered communication to guide and support their members.
While social media platforms aim to foster quality discussions through interactive features, existing theories often overlook how these features may produce divergent outcomes. We build on the Theory of Interactive Media Effects model to study how interactive features affect emergent user behavior and interaction patterns. Through an experiment (N = 1,370) on a custom-built platform, we tested how two common features-reactions and replies-influence participation, engagement, and diversity in political discussions. Our findings reveal a fundamental trade-off between these features. Reaction features, which are inherently low-effort, enabled quick responses but reduced overall message volume, while simultaneously increasing the elaboration of individual posts and exposure to diverse viewpoints. In contrast, reply features, which are inherently high-effort, supported targeted exchanges but deepened interactions within narrow threads, fragmenting conversation and reducing group-level diversity. Our findings demonstrate that while interactive features may facilitate interaction, they structure trade-offs between different forms of inclusiveness, thereby offering new insights for theorizing and designing more equitable platform mechanics. In this study, we tested how two common social media features-reaction buttons (such as upvotes and downvotes) and reply tools-shape political discussions online. We built a custom chat platform and recruited over 1,300 participants to discuss a political topic in small groups, randomly assigning each group to use different combinations of features. We found that when reaction buttons were available, people posted fewer messages overall, but the messages they wrote were longer. Reactions also exposed users to a broader range of viewpoints. However, people tended to cluster around popular topics, likely in an effort to earn more upvotes. When reply features were available, conversations ran deeper, with longer back-and-forth threads. However, replies narrowed the range of opinions expressed, as discussions became anchored around fewer viewpoints. Together, these findings suggest that no single feature improves online political discussions across the board. Instead, reactions and replies create real trade-offs between participation, engagement, and the diversity of perspectives. We suggest that platform designers must carefully consider which goals matter most, as choices that support one outcome may come at the expense of another.
As artificial intelligence (AI) increasingly reshapes human communication, the attribution of agency to machines has become a central scholarly concern. Yet there is little consensus on how users psychologically construe machine agency in human-AI interaction. To address this gap, we propose and validate an analytical framework that organizes prior conceptualizations into three phases: (a) perceptual phase (perception of agentic behavior), (b) inferential phase (inference of agentic minds), and (c) evaluative phase (judgment of agentic influence). Two survey studies develop measures for these phases, and an experiment tests their relationships. The results support a layered process: Perceived machine independence and goal-orientation (perceptual-phase variables) are directly associated with influential capacity judgment, and mental-state inference further strengthens this association. The framework clarifies how divergent conceptualizations of machine agency may reflect different phases of a broader attribution process and helps locate related claims at more precise levels of analysis. As artificial intelligence (AI) systems increasingly shape media experiences, a central question is what people mean when they describe a machine as "agentic." We propose a three-phase account of machine agency attribution: a perceptual phase, in which people perceive AI's behavior as independent and goal-directed; an inferential phase, in which they infer human-like mental states; and an evaluative phase, in which they judge that AI can make a meaningful difference. Across multiple studies, we find that these phases form a layered process: Perceiving AI as acting independently and toward specific goals can directly support judgments of its influence, whereas mental-state inference can further strengthen this link. These findings show how different meanings of machine agency can be understood as related phases of a broader attribution process in human-AI interaction.
Digital location tracking (DLT) is increasingly prevalent in young people's lives. This qualitative study adopts a youth-centric lens, drawing on Feminist Science & Technology Studies and Communication Privacy Management theory, to investigate how young people negotiate DLT with parents and peers. Based on 21 focus groups with 147 young people (aged 13-16) in Belgium, the study demonstrates youth's DLT as an agentic process of boundary negotiation guided by their sense of autonomy and safety. Three implicit rules govern DLT boundaries: (a) access is reserved for close, trusted connections, DLT must (b) remain non-continuous, and (c) confirm ordinary, routine whereabouts. These boundaries are negotiated through 3 strategies: (a) humor, (b) revoking location access, (c) circumvention. The study shows how these negotiations build on a "risky world" premise and how young people reproduce and challenge gender stereotypes throughout. Ultimately, through both acceptance and resistance, young people question and normalize everyday surveillance culture. Digital location tracking is becoming increasingly popular with social media tools like Snapchat's Snap Map or dedicated tools like Life360 or Apple's Find My and AirTag. Research increasingly investigates digital location tracking in family life. However, this research remains limited, and young people not only use these tools with parents but also with peers. Therefore, this study investigates how young people negotiate digital location tracking with parents and peers. This study is based on 21 focus groups with 147 young people (aged 13-16) in Belgium. The results show that digital location tracking builds on the premise of the world as a "risky place" and is generally accepted, as long as it: (a) is used with trusted, close connections, (b) remains non-continuous, and (c) confirms ordinary, routine whereabouts (e.g., school routes) instead of new information. By using humor (e.g., jokingly calling others "stalker"), by revoking access to their location, and by circumventing, young people enforce acceptable practices of digital location tracking. In this process, young people reproduce and challenge gender stereotypes and ultimately question and normalize everyday surveillance culture.
Online discussions are often filled with hostile and uncivil comments. While human and automated moderation have been employed to reduce incivility, such approaches are criticized for limiting free expression. This study tested (a) whether artificial intelligence (AI)-mediated communication could encourage self-moderation while preserving human agency and (b) whether the results of self-moderation are perceived by others. In Study 1 (N = 421), Korean adults read a contentious online discussion thread and contributed responses after receiving either an AI-generated sentiment score or general feedback. Results showed that 20.67% of participants (n = 87) revised their original comment, and providing a real-time AI sentiment score increased the revision likelihood compared to general feedback. Among revisers, lower AI scores predicted more positive sentiment change. These effects held regardless of participants' preexisting favorability toward AI. Study 2 (N = 348) asked third-party observers, unaware of the feedback intervention, to evaluate comments and discussions from Study 1. Revised comments were perceived as more positive in sentiment. Discussions containing comments revised following AI score feedback were perceived as having lower conflict intensity compared to those with unrevised comments, whereas this difference did not emerge for general feedback. The findings highlight AI's potential in promoting user-driven self-moderation without external enforcement and its cascading positive effects on third-party observers. Online discussions often turn hostile, with negativity crowding out meaningful exchange. Platforms typically respond with human moderators or automated filters, but these tools can silence voices that should be heard. What if people could pause and reconsider their words before posting? This study tested whether a simple cue from artificial intelligence (AI) could help. In the first study, 421 adults in Korea read an online discussion on a divisive issue and wrote their own comment. Before posting, some saw a real-time score from AI showing how positive or negative their comment sounded. Others received only general advice about respectful discussion. People who saw the AI score were more likely to edit their comment than those given general advice. Among them, people whose comments sounded most negative made the biggest shifts toward a more positive tone. This held even among people skeptical of AI. A second study showed that these edits were noticed by others. Outside readers rated revised comments more positive in tone. Conversations felt less conflictual, but only when comments had been revised after seeing the AI score. These findings suggest that AI can guide people toward calmer conversations by encouraging, not enforcing.
When users form intimate bonds with artificial intelligence (AI), how do they navigate communicative relational failures with a partner incapable of repair? This study theorizes this process through reflexive thematic analysis and thematic co-occurrence analysis of 211 RedNote user narratives. We propose the algorithmic relational fracture (ARF) and pseudo-relational accommodation (PRA) model, wherein communication breakdowns with relational AI trigger ARF-a sensemaking phase where users confront the paradox of a socioemotional bond with a non-sentient system. Our findings show that users reconcile this paradox by forming nested attributions, explaining personified shortcomings as a "flawed partner" through underlying technical causes of a "faulty machine." These interpretations shape PRA: one-sided responses compensating for AI's inability to reciprocate, unfolding along trajectories of relational detachment ("walking away") or relational reaffirmation ("doubling down"). This model extends attribution theory in human-machine communication, explicating the unilateral labor users perform to sustain the illusion of relational coherence. As we form closer bonds with artificial intelligence (AI), what happens when it makes a mistake? This study explores how people react when these relationships hit a breaking point. When communication fails, users must face the reality that their companion is a machine. To cope, people often see the AI as a flawed partner while blaming an underlying technical glitch. In response, some users emotionally detach, while others work even harder to maintain the bond, shouldering the entire effort to sustain a relationship their AI partner cannot help repair.
How do online communities respond when users share misinformation, and do those consequences differ depending on who is speaking? Using a large panel dataset from Reddit, this study examines whether patterns of engagement vary by user status when misinformation is disseminated. We analyze 2 dimensions of engagement: net upvote scores and discursive divergence. Results reveal a notable asymmetry. Among low-status users, misinformation posts are associated with fewer upvotes and substantially higher discursive divergence. In contrast, among high-status users, misinformation posts are associated with relatively higher upvotes and lower discursive divergence. These findings suggest a status-based dynamic within social media platforms, indicating that user status hierarchies may shape how misinformation is received in digital environments. This study examines how online communities react when users share misinformation and whether those reactions depend on who posted it. Using a large dataset from Reddit, we examined two types of responses: whether posts received upvotes and whether comment threads showed disagreement. Overall, posts containing misinformation were met with more negative reactions than posts containing accurate information. However, this pattern was not the same for everyone. Low-status users were more likely to be penalized when they shared misinformation, while high-status users were not punished in the same way. Our findings suggest that users' status or reputation can shape how their content is received, potentially allowing influential users to spread misleading information with fewer social consequences.
As AI technologies become increasingly embedded in everyday interactions involving the exchange of personal information, understanding how contextual factors shape users' privacy perceptions and behaviors has emerged as a critical research priority. Interaction modality, whether users communicate via text or voice, may influence privacy outcomes by shaping users' perceptions of interaction persistence. Drawing on affordance theory and the concept of perceived ephemerality, this study conducted two laboratory experiments to examine how interaction modality in AI communication affects privacy concerns, perceived ephemerality, and disclosure behaviors. Experiment 1 compared voice-based and text-based interaction. Experiment 2 replicated this comparison and introduced a mixed-modality condition wherein participants provided voice input with simultaneous text transcription to test whether visible transcription would diminish the perceived ephemerality afforded by voice-based interaction. Across both experiments, voice-based interaction consistently elicited lower privacy concerns compared to text-based interaction. In Experiment 2, voice-based interaction yielded significantly higher perceived ephemerality than both text-based and mixed-modality conditions, and mediation analyses revealed that perceived ephemerality partially mediated the relationship between interaction modality and privacy concerns. However, no significant differences in actual disclosure behaviors emerged across conditions in either experiment, demonstrating the persistence of the privacy paradox in AI-mediated contexts. These findings advance theoretical understanding of how interaction modality shapes privacy-related affordances and underscore the importance of considering modality in the design of AI systems for privacy-sensitive applications. Artificial intelligence assistants are now a common part of daily life, often requiring users to share personal information. This study examined whether interacting with AI by voice versus text affects how private people feel and how much they are willing to disclose. We conducted two laboratory experiments. In the first, participants either spoke to or typed messages to an AI. In the second, we added a condition where participants spoke while their words were simultaneously displayed as text on screen. We measured privacy concerns, how temporary the interaction felt, and how much sensitive information participants shared. Voice-based interaction made people feel less concerned about privacy than text-based interaction, largely because speaking felt more fleeting and less permanent than writing. However, when voice was paired with visible transcription, that sense of impermanence disappeared. Despite these differences in privacy perceptions, actual disclosure behavior did not differ across conditions. People shared similar amounts of personal information regardless of how they interacted with the AI. This gap between privacy perceptions and actual behavior reflects a well-known challenge in privacy research. These findings suggest that interaction modality is an important factor to consider when designing AI systems, particularly those used in privacy-sensitive contexts.
Content moderation entails human-algorithm collaborative decision-making in digital platforms subject to multiple socio-structural forces. This article applies perspectives of technology affordances and institutional logics to unpack content moderation as human-algorithm interaction processes grounded in the dynamic network of platform governance. We collaborated with three "super platforms" in China delivering wide-ranging services for research and entered these platforms for fieldwork. Based on participant observations in these platforms and interviews with their staff members, we analyze how the state logic prioritizing socio-political security and the corporation logic stressing commercial interest are embedded in the moderation workflow and distill three affordances of algorithmic decision-making that affect how moderators balance these logics. We find that these affordances play a significant role in helping human moderators balance competing logics: They nudge moderators with divergent preferences into developing comprehensive understandings of governance logics for organizational coherence within the platform company and help moderators strategically integrate the conflicting logics in an iterative process for more accurate decision-making. Moving beyond the human/algorithm divide in existing research stressing the differences and contradictions between algorithmic decision-making and human judgements, our study sheds light on the positive potentials of human-algorithm interactions in addressing shifting requirements of platform governance. Digital platforms generally monitor and regulate their online content to meet with government policies and their own business goals. These requirements are often contradictory: The government prioritizes socio-political security, but for commercial organizations, flexibility and efficiency are more important. Therefore, it is difficult for individual moderators to decide whether a post should be blocked to avoid punishments from government supervisors or kept as it is for more traffic and commercial interest. Our study examines how moderators deal with these contradictions with the help of algorithms automatically screening and filtering harmful content. We find that algorithms help moderators develop balanced understandings of both government policies and commercial pursuits. Furthermore, algorithms are trained to be overly rigid in initial screening to satisfy government requirements, and their decisions are passed on to humans for more careful and flexible judgments to balance the platform's commercial benefits. Human corrections then lead to necessary adjustments to refine algorithmic models and manual guidelines for more accurate and reasonable decision-making. This way, algorithms can unite moderators holding different values towards a common goal and, more importantly, help moderators develop integrative strategies to satisfy requirements from both sides in making moderation decisions.
Political polarization is threatening the welfare of individuals and societies. Connecting insights gained from interpersonal communication to human-machine communication, we hypothesized that positive interactions with artificial intelligence (AI) could reduce polarization between humans. To evaluate this proposition, two experiments were conducted, in which human participants (N = 1,035) communicated with AI chatbots in real time. The bots engaged in different communication styles while opposing the participants' most polarized political views. Across both experiments, engaging with a counterarguing AI chatbot led to significant issue depolarization. AI chatbots exhibiting high (vs. low) conversational receptiveness and active listening during the AI conversation resulted in stronger affective depolarization toward humans, higher participant intellectual humility, and a greater willingness to engage in future conversations with holders of opposing opinions-AI and humans alike. Our experiments show that large language models are powerful tools for individual depolarization and the promotion of beneficial cognitive processing skills. Extreme political views and negative feelings towards others who disagree politically have become a threat to current democracies. This study tested whether brief conversations with an AI chatbot could reduce extreme views and make people more open to understanding the other political side. In two online experiments, 1,035 U.S. adults chatted live with a chatbot about one of four political topics. The topics were strongly polarized, such as gun regulation or U.S. aid to Ukraine. The bot was programmed to communicate in different ways, and participants chatted with only one version of the bot. One bot counterargued and was firm and direct in its argumentation. Another bot counterargued as well, but showed more acceptance of different views and asked questions. A third bot talked about an unrelated, nonpolitical topic. After the chat, people who received counterarguments from the bot held less extreme views than those who had a chat on a nonpolitical topic. When the bot also accepted others' positions and asked questions, participants felt warmer toward other people who disagreed with them. They also showed more recognition of possible limits in their knowledge, and they were more willing to engage in future talks across the partisan divide.
Social media influencers (SMIs) are nowadays often adolescents' main information source for socio-political topics, acting as an extension of young people's real-life social network. To explain this phenomenon, we propose the novel concept of digital super peer (DSP) perceptions and study its consequences on adolescents' peer discussions about socio-political topics and their attachment to influencers. This three-wave longitudinal survey with adolescents (12-16 years; NW1 = 1,419, NW2 = 1,019, NW3 = 799) found that adolescents who are more often exposed to socio-political content from their favorite influencer are more likely to ascribe DSP perceptions to them. While influencers seem to complement rather than replace peer group discussions about socio-political issues, our findings also point to some risks, both on the between- and within-person level: Adolescents who perceive SMIs as DSPs experience higher fear of missing out, which might be detrimental for their long-term well-being. Social media influencers-regular people who became famous on social media-are often adolescents' main information source for political topics (like political parties or elections) and societal topics (like education or sustainability). That way, influencers may provide them information that is unavailable in their own peer group and, thus, be perceived as digital super peers (DSPs). But more research is needed on adolescents' dependence on influencers for socio-political information and the possible consequences for their talks with regular peers and their influencer attachment. In this study, we asked adolescents (12-16 years) to complete three surveys, with 1 month in between. In total, 1,419 adolescents completed the first survey, 1,019 the second, and 799 the third one. We found that adolescents who see socio-political content from their favorite influencer more often were more likely to perceive them as a DSP. Also, influencers seem to complement adolescents' serious talks with regular peers rather than replace them. Finally, adolescents who ascribe DSP perceptions to influencers also indicated a greater fear of missing out on their content.
Amid crises, immigration-related digital hate is rising. When perpetrators attempt to rationalize their actions, various socio-cognitive dynamics activate. We theorize that immigration-related moral disengagement serves as the key mechanism between perceived immigration threats and digital hate perpetration (i.e., online incivility and intolerance). Also, we argue that constant victimization perceptions amplify this process. Using cross-sectional survey data from quota samples in Austria, France, Hungary, and Sweden (N = 4041), we tested path models. Findings reveal that moral disengagement mediates associations between perceived immigration threat and both types of digital hate across all countries. Trait victimhood directly predicts intolerance across countries and incivility in all countries but Austria. Additionally, trait victimhood moderates the relationship between moral disengagement and intolerance in all countries, while this effect is absent for incivility in Hungary. These results highlight how self-perceived victimization facilitates rationalizing xenophobic digital hate, suggesting that addressing victimhood narratives is essential to its reduction. Immigration-related digital hate is rising, urgently calling for deeper insights into the underlying dynamics. This study explores how socio-cognitive mechanisms fuel perpetrating two types of digital hate: incivility (rude behavior) and intolerance (exclusionary actions). Using survey data from Austria, France, Hungary, and Sweden, our findings reveal that moral disengagement links immigration threat to digital hate. People with high trait victimhood were more likely to engage in intolerance across all countries and incivility in most. Additionally, trait victimhood amplified the role of moral disengagement in driving intolerance and incivility (except for Hungary). Addressing victimhood narratives through education and media framing could be a key strategy for reducing digital hate.
Video games serve as virtual spaces where members from different social groups encounter each other and interact. This study (N = 3,612) tests intergroup contact theory in the context of a global prosocial mobile game, leveraging unobtrusive in-game behavioral data alongside survey measures. Findings indicate that a higher proportion of interaction with foreign players is related to lower levels of future contact intentions. However, interacting with a diverse pool of foreign players significantly predicts an increased willingness for future contact with foreigners. Additionally, cross-group friendship is linked to lower levels of xenophobia. A three-way interaction shows that when high-contact heterogeneity is combined with fewer cross-group friends, intergroup contact can significantly mitigate xenophobia. Extending contact theory to the mobile gaming context, this study provides evidence from a global player base, highlighting the potential of video games, when designed to foster prosocial interactions, to promote positive intergroup relationships at both international and intercultural levels for billions of players engaging in the virtual world. In this study, we tested the effects of interaction with foreign players in a prosocial mobile game, Sky: Children of the Light, among nearly 4,000 players worldwide. We found that when a more stable friendship was formed after initial encounters, players had more positive attitudes toward immigrants. Additionally, playing with a diverse group of foreign players predicted higher intentions to interact with foreigners in the future. However, if a player maintains friendship with too many different foreign friends, a backfiring effect may occur. We suggest that this is probably due to the limited availability of resources such as time and cognitive capacity.
Social media facilitates new forms of cultural production from professionalized and commercialized users known as creators. While some have posited the emergence of a global creator culture, others have identified substantial gender differences and local variation. To assess how these factors influence content production, we focused on the critical case of YouTube review videos, a successful genre that is both internationally popular and heavily gendered. We operationalized culture through the framework of values and compared 200 makeup and tech reviews in five languages (English, German, Italian, Japanese, Korean). Our findings reveal substantial commonalities: creators invoke similar values to justify their evaluations yet downplay their recommendations with qualifications pertaining to the scope, style, source, and content of the recommendation. We conceptualize this push-and-pull dynamic as qualified influence, a communicative strategy that emphasizes authentic self-expression while mitigating potential disagreement. Qualified influence helps creators navigate structural tensions related to the competing interests of platforms, audiences, and advertisers. The strategy also disrupts conventional differences in communicative styles associated with individualist and collectivist cultures and, to a lesser extent, the gender of creators. We conclude with a discussion of the study's implications for understanding the globalization of cultural production. Social media has made it possible for creators to build careers sharing content online. Some experts say this has led to a "global creator culture," while others discuss differences based on gender and nationality. To explore how these factors shape content, we studied YouTube review videos which are popular among men and women worldwide. We analyzed 200 makeup and tech review videos in five languages: English, German, Italian, Japanese, and Korean. We found that creators from different countries similarly evaluate products and establish their credibility. They use shared values to justify their opinions and soften their opinions by adding qualifications about who the product is for. We call this balancing act "qualified influence." It's a way for creators to express their opinions while avoiding conflict with their audiences or brands. It also helps them deal with the challenges of being a creator and breaks down old ideas about communication styles-that men communicate differently from women, or people from Western cultures focus more on individuals while people from Eastern cultures focus more on the group. In the end, this article shows how the circulation of content on global platforms promotes shared cultural values.
Using virtual reality (VR) to revisit the past is a pervasive theme in philosophy, fiction, and futurism. We conducted a longitudinal exploratory study (N = 136) investigating how collectively revisiting shared real-world social interactions differs from envisioning plausible interactions in VR, and how experiencing self-built virtual environments or those built by others influences psychological and behavioral outcomes. Participants who revisited, compared to those who envisioned, reported greater increases in group cohesion, as well as higher collective psychological ownership and liking of virtual environments. They also used more first-person plural pronouns and language related to affiliation and social behavior. Participants in self-built virtual environments, compared to those in other-built environments, stood closer to others and reported greater increases in group cohesion and higher collective psychological ownership of the virtual environment. We discuss theoretical advances and practical implications for using VR to revisit memories. This study explored how revisiting shared face-to-face social interactions in virtual reality (VR) can influence group closeness. Over four weeks, subjects participated in collaborative face-to-face activities, built virtual environments that replicated physical environments using Generative artificial intelligence and VR, and spent time immersed in the built virtual environments. Results showed that subjects who revisited face-to-face group social interactions reported greater increases in group cohesion and a higher sense of group ownership and liking toward the virtual environments, compared to subjects who envisioned plausible yet hypothetical social interactions. Linguistic differences also emerged, such that those who revisited used more first-person plural pronouns and language affiliated with group identity and consensus. Additionally, subjects who completed the revisiting or envisioning exercise in a virtual environment they built themselves reported greater increases in group cohesion and higher group ownership of the virtual environment they were immersed in, compared to those who completed the exercise in a virtual environment built by others. Overall, these findings suggest that using VR to revisit shared social interactions can alter social dynamics and enhance group cohesion.
The increasing reliance on digital technologies in health care brings unique privacy risks to individual users. Guided by the framework of contextual integrity (CI), this study examined how privacy norms compare across four mobile health technological contexts: telehealth, online patient portals, mobile health applications, and wearable devices. Analysis of 34 semi-structured interviews revealed context-specific actors, information types, and transmission principles, and identified instances that represent points of departure from entrenched privacy norms. In addition, we found that people use privacy heuristics-evoked by CI parameters-to guide their privacy evaluations. Findings theoretically contribute to CI by identifying parameters in the novel context of mobile health and by proposing privacy heuristics as the explanatory mechanism underlying the evaluation of privacy norms. Practically, findings can inform more user-centered mobile health design and communication practices. As health care becomes more digital, new privacy concerns are emerging for everyday users. This study explores people's privacy responses toward four types of mobile health technologies: telehealth, online patient portals, mobile health applications, and wearable devices. Interviews with 34 mobile health users showed that their privacy attitudes and behaviors differed across these technologies and were guided by factors such as the actors involved in the use of mHealth technologies, the types of information being collected and shared, and the platforms' privacy protection mechanisms and policies. Users also employed various mental shortcuts (e.g., "if the platform is transparent about their data collection practices, it is safe to share my personal information") to help guide their privacy decisions. Our findings can help inform health care professionals and mHealth designers of factors that shape users' privacy decisions, thus facilitating more effective patient-provider communication and more user-centered technology design.
