Large language models often produce biased or stereotypical outputs. One way to reduce this possibility is to be more inclusive in our prompts, but doing so may not come naturally to most users. Therefore, we designed a tool that coaches users to write more inclusive prompts—a strategy that leverages design friction to provide a media literacy intervention. Data from a user study (N=344) show that compared to no coaching, inclusive prompt coaching directly increased users’ awareness of algorithmic bias and their perceived prompting efficacy. It also indirectly enhanced their trust in the system and perceived trust calibration through cognitive elaboration. However, inclusive prompt coaching resulted in a less satisfying user experience. These findings have implications for ethical interventions in prompting for better communicating and combating algorithmic bias. We discuss the benefits and limitations of inclusive prompt coaching, as well as ways to balance usability for long-term adoption of generative AI systems.

The quality of online wellness communities is difficult to explain using simple growth metrics. However, an empirical understanding of the structural and relational factors that define quality has proven elusive. To fill this gap, we analyzed over 17 million posts from 56 Reddit wellness communities between 2023 and 2024 by applying the sociability-usability framework, measuring user-based factors (e.g., User Retention, Gini Coefficient) and content-based factors (e.g., Interactivity, Quasi-Quality Index), while also examining the impact of identity (Linguistic Distinctiveness and Topic Drift). Our analysis confirmed that quality depends on a stable core user base and meaningful reciprocal exchanges rather than conversation volume. A strong identity creates a trade-off, enhancing internal cohesion while acting as a barrier to new user inflows. Overall, we challenge the conventional ‘growth=success’ paradigm, establishing structural sustainability as the core mechanism for defining quality in online wellness communities and offering a new framework for their design and evaluation.

Reliable relevance modeling and ranking are pivotal to legal information retrieval (IR), where graded relevance judgments are indispensable yet prohibitively expensive to acquire. Despite their importance, expert-validated graded relevance benchmarks remain scarce, especially for non-English statutory-law jurisdictions where citation practices differ fundamentally from common-law paradigms. We introduce KCC, a publicly available and reusable benchmark resource for legal IR in Korean civil law. KCC is constructed from 38,372 publicly accessible court decisions and comprises 2,942 query–candidate case pairs annotated with expert-validated four-level relevance labels. These labels jointly capture factual similarity and legal reasoning, explicitly addressing the absence of systematic citation links in statutory-law systems. To facilitate reproducibility, we provide a detailed description of the dataset construction pipeline, annotation protocol, relevance criteria, and inter-annotator agreement statistics validated by legal professionals. The annotation guidelines are designed to be adaptable to other statutory-law and non-English legal IR settings, enabling future extensions beyond the current dataset. We further present a set of reference benchmark experiments using representative retrieval approaches, including traditional IR models, neural rankers, and LLM-based prompting methods, to indicate recommended usage and evaluation practices with KCC. We believe that KCC provides a long-term resource for the research community to study relevance modeling, ranking behavior, and evaluation methodology in domain-specific legal IR scenarios beyond English and common-law traditions.

Interactive features have become ubiquitous in mobile apps as they compete for users’ attention. This raises an important question: Is interactivity a ‘dark pattern’, tricking users into giving away their personal information without thinking about consequences? Are some types of interactivity more persuasive than others? We addressed these questions with a 2 (message interactivity: low, high) X 3 (modality interactivity: absence, low, high) between-subjects experiment (N = 216) designed to test hypotheses derived from the Theory of Interactive Media Effects (TIME) and the Heuristic-Systematic Model (HSM) of information processing. Our data show that both modality interactivity and message interactivity had positive effects on attitudes toward the app and intentions to use it by directing users’ attention away from privacy concerns to heightened perceived playfulness. We discuss the ethical implications of the double-edged nature of interactive technology for both users and developers.

Internet Gaming Disorder (IGD) is officially recognized as a mental health condition by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), a standard for diagnosing mental health conditions. However, current diagnostic methods rely on subjective self-reports, which lack objectivity and consistency. This study proposes the Gaming Disorder Detection (GDD) framework, using data from Reddit’s StopGaming subreddit to provide an objective and scalable IGD detection approach. Sentences extracted from Reddit posts were labeled using a Large Language Model (LLM) based on DSM-5 and IGD-20 criteria, reducing bias and improving diagnostic consistency. The labeled dataset was then analyzed with a Graph Neural Network (GNN) to predict IGD patterns in new data. By combining LLM-based labeling and GNN-based analysis, the study shows the potential of integrating DSM-5 and IGD-20 criteria into a data-driven framework for IGD detection, offering a novel tool for clinicians and experts to objectively and systematically evaluate gaming disorder.

Xinyu Wang, Wenbo Zhang, Sai Koneru, Hangzhi Guo, Bonam Mingole, S. Shyam Sundar, Sarah Rajtmajer, Amulya Yadav. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The popularity of automated features, such as autocorrect, reflects an interesting paradox in digital media use: while users appreciate the interactivity afforded by these media, they also seem to enjoy passively observing the system perform the interaction on their behalf. We aim to understand this paradox by using the concept of interpassivity and exploring the primary gratifications users seek in automated features. Following the research methods in U&G research, we first conducted three focus groups to generate a list of 66 gratification items, which were subjected to exploratory factor analysis in a survey study (N = 498). Results show that convenience, user control, and user profiling are three distinct gratifications of automated feature usage. Furthermore, user control is universally desired across features, and user profiling motivates the use of all automated features. We discuss the implications of these findings for U&G research and interface design of automated features.

Recommender systems (RS) have become increasingly vital for guiding health actions. While traditional systems filter content based on either demographics, personal history of activities, or preferences of other users, newer systems use social media information to personalize recommendations, based either on the users’ own activities and/or those of their friends on social media platforms. However, we do not know if these approaches differ in their persuasiveness. To find out, we conducted a user study of a fitness plan recommender system (N = 341), wherein participants were randomly assigned to one of six personalization approaches, with half of them given a choice to switch to a different approach. Data revealed that social media-based personalization threatens users’ identity and increases privacy concerns. Users prefer personalized health recommendations based on their own preferences. Choice enhances trust by providing users with a greater sense of agency and lowering their privacy concerns. These findings provide design implications for RS, especially in the preventive health domain.

To promote data transparency, frameworks such as CrowdWorkSheets encourage documentation of annotation practices on the interfaces of AI systems, but we do not know how they affect user experience. Will the quality of labeling affect perceived credibility of training data? Does the source of annotation matter? Will a credible dataset persuade users to trust a system even if it shows racial biases in its predictions? To find out, we conducted a user study (N = 430) with a prototype of a classification system, using a 2 (labeling quality: high vs. low) × 4 (source: others-as-source vs. self-as-source cue vs. self-as-source voluntary action, vs. self-as-source forced action) × 3 (AI performance: none vs. biased vs. unbiased) experiment. We found that high-quality labeling leads to higher perceived training data credibility, which in turn enhances users’ trust in AI, but not when the system shows bias. Practical implications for explainable and ethical AI interfaces are discussed.

Three of the most common approaches used in recommender systems are content-based filtering (matching users’ preferences with products’ characteristics), collaborative filtering (matching users with similar preferences), and demographic filtering (catering to users based on demographic characteristics). Do users’ intuitions lead them to trust one of these approaches over others, independent of the actual operations of these different systems? Does their faith in one type or another depend on the quality of the recommendation, rather than how the recommendation appears to have been derived? We conducted an empirical study with a prototype of a movie recommender system to find out. A 3 (Ostensible Recommender Type: Content vs. Collaborative vs. Demographic Filtering) x 2 (Recommendation Quality: Good vs. Bad) experiment (N=226) investigated how users evaluate systems and attribute responsibility for the recommendations they receive. We found that users trust systems that use collaborative filtering more, regardless of the system's performance. They think that they themselves are responsible for good recommendations but that the system is responsible for bad recommendations (reflecting a self-serving bias). Theoretical insights, design implications and practical solutions for the cold start problem are discussed.

This paper introduces the concept of “news informatics” to refer to journalistic presentation of big data in online sites. For users to be engaged with such data-driven public information, it is important to incorporate interactive tools so that each person can extract personally relevant information. Drawing upon a communication model of interactivity, we designed a data-rich site with three different types of interactive features—namely, modality interactivity, message interactivity, and source interactivity—and empirically tested their relative and combined effects on user engagement and user experience with a 2 (modality) × 3 (source) × 2 (message) field experiment (N =166). Findings shed light on how interface designers, online news editors and journalists can maximize user engagement with data-rich news content. Certain interactivity combinations are found to be better than others, with a structural equation model (SEM) revealing the underlying theoretical mechanisms and providing implications for the design of news informatics.

Active listening is a well-known skill applied in human communication to build intimacy and elicit self-disclosure to support a wide variety of cooperative tasks. When applied to conversational UIs, active listening from machines can also elicit greater self-disclosure by signaling to the users that they are being heard, which can have positive outcomes. However, it takes considerable engineering effort and training to embed active listening skills in machines at scale, given the need to personalize active-listening cues to individual users and their specific utterances. A more generic solution is needed given the increasing use of conversational agents, especially by the growing number of socially isolated individuals. With this in mind, we developed an Amazon Alexa skill that provides privacy-preserving and pseudo-random backchanneling to indicate active listening. User study (N = 40) data show that backchanneling improves perceived degree of active listening by smart speakers. It also results in more emotional disclosure, with participants using more positive words. Perception of smart speakers as active listeners is positively associated with perceived emotional support. Interview data corroborate the feasibility of using smart speakers to provide emotional support. These findings have important implications for smart speaker interaction design in several domains of cooperative work and social computing.

The outbreak of COVID-19 forced schools to swiftly transition from in-person classes to online or remote offerings, making educators and learners alike rely on online videoconferencing platforms. Platforms like Zoom offer audio-visual channels of communication and include features that are designed to approximate the classroom experience. However, it is not clear how students' learning experiences are affected by affordances of the videoconferencing platforms or what underlying factors explain the differential effects of these affordances on class experiences of engagement, interaction, and satisfaction. In order to find out, we conducted two online survey studies: Study 1 (N = 176) investigated the effects of three types of videoconferencing affordances (i.e., modality, interactivity, and agency affordances) on class experience during the first two months after the transition to online learning. Results showed that usage of the three kinds of affordances was positively correlated with students' class engagement, interaction, and satisfaction. Perceived anonymity, nonverbal cues, and comfort level were found to be key mediators. In addition, students' usage of video cameras in class was influenced by their classmates. Study 2 (N = 256) tested the proposed relationships at a later stage of the pandemic and found similar results, thus serving as a constructive replication. This paper focuses on reporting the results of Study 1 since it captures the timely reactions from students when they first went online, and the second study plays a supplementary role in verifying Study 1 and thereby extending its external validity. Together, the two studies provide insights for instructors on how to leverage different videoconferencing affordances to enhance the virtual learning experience. Design implications for digital tools in online education are also discussed.

AI systems collect our personal information in order to provide personalized services, raising privacy concerns and making users leery. As a result, systems have begun emphasizing overt over covert collection of information by directly asking users. This poses an important question for ethical interaction design, which is dedicated to improving user experience while promoting informed decision-making: Should the interface tout the benefits of information disclosure and frame itself as a help-provider? Or, should it appear as a help-seeker? We decided to find out by creating a mockup of a news recommendation system called Mindz and conducting an online user study (N=293) with the following four variations: AI system as help seeker vs. help provider vs. both vs. neither. Data showed that even though all participants received the same recommendations, power users tended to trust a help-seeking Mindz more whereas non-power users favored one that is both help-seeker and help-provider.

Studies show that users do not reliably click more often on headlines classified as clickbait by automated classifiers. Is this because the linguistic criteria (e.g., use of lists or questions) emphasized by the classifiers are not psychologically relevant in attracting interest, or because their classifications are confounded by other unknown factors associated with assumptions of the classifiers? We address these possibilities with three studies—a quasi-experiment using headlines classified as clickbait by three machine-learning models (Study 1), a controlled experiment varying the headline of an identical news story to contain only one clickbait characteristic (Study 2), and a computational analysis of four classifiers using real-world sharing data (Study 3). Studies 1 and 2 revealed that clickbait did not generate more curiosity than non-clickbait. Study 3 revealed that while some headlines generate more engagement, the detectors agreed on a classification only 47% of the time, raising fundamental questions about their validity.

"Always-on" smart speakers have raised privacy and security concerns, to address which vendors have introduced customizable privacy settings. But, does the act of customizing one's privacy preferences have any effects on user experience and trust? To address this question, we developed an app for Amazon Alexa and conducted a user study (N = 90). Our data show that the affordance to customize privacy settings enhances trust and usability for regular users, while it has adverse effects on power users. In addition, only enabling privacy-setting customization without allowing content customization negatively affects trust among users with higher privacy concerns. When they can customize both content and privacy settings, user trust is highest. That is, while privacy customization may cause reactance among power users, allowing privacy-concerned individuals to simultaneously customize content can help to alleviate the resultant negative effect on trust. These findings have implications for designing more privacy-sensitive and trustworthy smart speakers.

Online users' attitudes toward privacy are context-dependent. Studies show that contextual cues are quite influential in motivating users to disclose personal information. Increasingly, these cues are embedded in the interface, but the mechanisms of their effects (e.g., unprofessional design contributing to more disclosure) are not fully understood. We posit that each cue triggers a specific "cognitive heuristic" that provides a rationale for decision-making. Using a national survey (N = 786) that elicited participants' disclosure intentions in common online scenarios, we identify 12 distinct heuristics relevant to privacy, and demonstrate that they are systematically associated with information disclosure. Data show that those with a higher accessibility to a given heuristic are more likely to disclose information. Design implications for protection of online privacy and security are discussed.

Public speaking anxiety is one of the most common social phobias. We explore the feasibility of using a conversational agent to reduce this anxiety. We developed a public-speaking tutor on the Amazon Alexa platform that enables users to engage in cognitive reconstruction exercises. We also investigated how the sociability of the agent might affect its performance as a tutor. A user study of 53 college students with fear of public speaking showed that the interaction with the agent served to assuage pre-speech state anxiety. Agent sociability improved the sense of interpersonal closeness, which was associated with lower pre-speech anxiety. Moreover, sociability of the agent increased participants' satisfaction and their willingness to continue engagement. Our findings, thus, have implications not only for addressing public speaking anxiety in a scalable way but also for the design of future conversational agents using smart speaker platforms.

Clickbait is an attractive yet misleading headline that lures readers to commit click-conversion. Development of robust clickbait detection models has been, however, hampered due to the shortage of high-quality labeled training samples. To overcome this challenge, we investigate how to exploit human-written and machine-generated synthetic clickbaits. We first ask crowdworkers and journalism students to generate clickbaity news headlines. Second, we utilize deep generative models to generate clickbaity headlines. Through empirical evaluations, we demonstrate that synthetic clickbaits by human entities and deep generative models are consistently useful in improving the accuracy of various prediction models, by as much as 14.5% in AUC, across two real datasets and different types of algorithms. Especially, we observe an improvement in accuracy, up to 8.5% in AUC, even for top-ranked clickbait detectors from Clickbait Challenge 2017. Our study proposes a novel direction to address the shortage of labeled training data, one of fundamental bottlenecks in supervised learning, by means of synthetic training data with reinforced domain knowledge. It also provides a solution for distinguishing between bot-generated and human-written clickbaits, thus aiding the work of moderators and better alerting news consumers.

In this day and age of identity theft, are we likely to trust machines more than humans for handling our personal information? We answer this question by invoking the concept of "machine heuristic," which is a rule of thumb that machines are more secure and trustworthy than humans. In an experiment (N = 160) that involved making airline reservations, users were more likely to reveal their credit card information to a machine agent than a human agent. We demonstrate that cues on the interface trigger the machine heuristic by showing that those with higher cognitive accessibility of the heuristic (i.e., stronger prior belief in the rule of thumb) were more likely than those with lower accessibility to disclose to a machine, but they did not differ in their disclosure to a human. These findings have implications for design of interface cues conveying machine vs. human sources of our online interactions.

Modern mobile apps aim to provide personalized services without appearing intrusive. A common strategy is to let the user initiate the service request (e.g., "click here to receive coupons for your favorite products"), a practice known as ?overt personalization." Another strategy is to assuage users' privacy concerns by being transparent about how their data would be collected, utilized and stored. To test these two strategies, we conducted a 2 (Personalization: Overt vs. Covert) x 2 (Transparency: High vs. Low) factorial experiment, with a fifth control condition. Participants (N=302) interacted with GreenByMe, a prototype of an eco-friendly mobile application. Data show that overt personalization affects perceived control. Significant three-way interactions between power usage, perceived overt personalization and perceived information transparency was seen on perceived ease of use, trust in the app, user engagement and behavioral intention to use the app in the future. In addition, results reveal that perceived information transparency also promotes trust, which is negatively linked with privacy concerns and positively correlated with user engagement and product involvement.

Parallax scrolling is a popular web technique used widely in product presentation websites, but its effect on user experience is unknown. Parallax scrolling allows the object displayed in the foreground to move faster than the object displayed in the background, so as to create a 3D effect. Dimensionality and scrolling (modality interactivity) are two major characteristics of this technique. A 2 (dimension) × 2 (modality interactivity) between-subjects (N = 295) online experiment revealed that parallax scrolling influences user engagement through two routes: the cue route (perceived coolness, perceived vividness) and the action route (natural mapping, perceived ease of use). User engagement is in turn associated with positive attitudes and behavioral intentions toward the website and the product. The theoretical contribution of the study is it examined interface feature of parallax scrolling on both user perceptions and user actions using the theory of interactive media effects (TIME). The practical contribution of the study is it provides a roadmap to web developers to emphasize affective factors (i.e., vividness, coolness) rather than cognitive factors (i.e., natural mapping, perceived usefulness) when it comes to improving the user experience of the product presentation websites.

Health forums and support groups depend on participant self-disclosure for their success, but the sensitive nature of personal health concerns raises privacy concerns that may constrain what users are willing to reveal. To address this issue, we explore the impact of visual cues designed to convey (1) two facets of social influence-crowd size and social network connectivity-and (2) provide a frame designed to enhance the forum's sense of community. A 3 (Cue type: Crowd, Connectivity, None) x 2 (Framing) factorial experiment (N = 218) showed that cues implying greater crowd size and connectivity lead to more self-disclosure of sensitive information, and higher intentions to revisit the community. Further, user belief in the community-building heuristic positively predicts self-disclosure and intentions, while also moderating the effect of the connectivity cue in a direction which implies that the cue encourages disclosure by triggering the community-building heuristic. Implications for the design of online groups are discussed.

Users often react negatively towards applications that track their personal information, even though they have consented to such tracking by hitting the "I Agree" button on the application's end user license agreement (EULA). This is because most users do not read the EULA carefully. The language and presentation of EULAs are often dull, dense and inaccessible. Researchers have proposed design options for heightening comprehension of EULA content, but the effectiveness of these suggestions is unclear. To address this gap, we conducted an experiment that examined how users' attitudes towards EULAs are affected by paraphrased and forced EULA formats. Paraphrased EULA presentations increased the time spent on reading the EULA. Moreover, they elicited more positive attitudes toward the EULA, which in turn predicted better comprehension. These findings hold implications for design of EULAs by showing that complex content displayed in simple terms across multiple windows can increase reader comprehension.

Does assembling one's robot enhance the quality of our interaction with it? And, does it matter whether the robot is a utilitarian tool or a socially interactive entity? We examined these questions with a 2 (Assembler: Self vs. Others) × 2 (Expectation Setting/Framing: Task-oriented robot vs. Interaction-oriented robot) between-subjects experiment (N = 80), in which participants interacted with a humanoid desktop robot (KT-Gladiator 19). Results showed that participants tended to have more positive evaluations of both the robot and the interaction process when they set it up themselves, an effect that is positively mediated by a sense of ownership and a sense of accomplishment, and negatively mediated by perceived process costs of setting up the robot. They also tended to evaluate the robot and the interaction more positively when they expected it to be task-oriented rather than interaction-oriented. Implications for theory and design of robots are discussed.

Horizontal swiping has become a common interaction technique on smartphones, yet many mobile websites do not incorporate this functionality, instead requiring users to scroll down or click on buttons, arrow keys, and other hotspots. This is perhaps because user studies comparing swiping with other interaction techniques are scarce. An experiment was conducted (N = 252) to inform this issue by investigating whether and how adding the swiping functionality to a tap-only mobile website influences users’ intentions to use the website. The data revealed that the addition of swiping technique positively affected behavioral intentions to use the website. Perceived enjoyment was a key variable that explained this effect. These findings have design implications for mobile websites.

When information and communication technologies are deployed for development (ICTD), a necessary condition for success is their acceptance and adoption. Recipients should trust a given ICT and believe in its welfare potential. However, certain features of modern ICTs, such as collecting and storing user information, may serve to undermine user trust in ICTs. We examine this possibility by investigating whether technological affordances of the electronic voting machine (EVM) trigger user perceptions about the machine and affect faith in democratic institutions. A survey (N=179) of Indian voters in New Delhi on the day of general elections in 2014 reveals that interactivity and recordability are negative predictors of trust in EVMs. Interactive machines were also considered less cool and less dependable. Theoretical and design implications of the findings are discussed.

Museums lean heavily on recent developments in communication technologies to create an authentic experience for online visitors of its galleries. This study examines whether three specific affordances of communication technology—customization, interactivity, and navigability—can provide the personal, social, and physical contexts, respectively, that are necessary for ensuring an enjoyable museum experience. A 2 (presence vs. absence of customizable gallery) × 2 (presence vs. absence of live-chat with others) × 2 (presence vs. absence of 3D navigational tool) between-subjects factorial experiment (N = 126) found that although each affordance is associated with distinct psychological benefits (customization with sense of agency and control, interactivity with reciprocity, and navigability with perceived reality), combining them on the same interface tends to undermine these benefits. In addition, power usage moderates the effectiveness of each affordance on the interface. Theoretical and practical implications are discussed.

In order to address the increased privacy and security concerns raised by mobile communications, designers of mobile applications and websites have come up with a variety of warnings and appeals. While some interstitials warn about potential risk to personal information due to an untrusted security certificate, others attempt to take users' minds away from privacy concerns by making tempting, time-sensitive offers. How effective are they? We conducted an online experiment (N = 220) to find out. Our data show that both these strategies raise red flags for users - appeals to instant gratification make users more leery of the site and warnings make them perceive greater threat to personal data. Yet, users tend to reveal more information about their social media accounts when warned about an insecure site. This is probably because users process these interstitials based on cognitive heuristics triggered by them. These findings hold important implications for the design of cues in mobile interfaces.

From scrolling and clicking to dragging, flipping, sliding, hovering, and zooming, the wide array of interaction techniques has vastly expanded the range of user actions on an interface. Each of these interaction techniques affords a distinct action. But do these techniques differ in their ability to engage users and contribute to their user experience? Furthermore, do they affect how users view the content and how much they learn from it? We address these questions via two between-subjects laboratory experiments. Study 1 (N = 128) investigated the relative effects of six on-screen interaction techniques (click-to-download, drag, mouseover, slide, zoom, and 3D carousel) on users' assessment of—as well as their engagement with—an informational website. The site for each condition was identical in content and design, except for the interaction technique used, so that we could isolate the effects of each technique on various cognitive, attitudinal and behavioral outcomes. Study 2 (N = 127) examined the relative effects of four combinations of interaction techniques (slide+click, slide+mouseover, drag+mouseover, and drag+zoom) on the same dependent variables. Data from Study 1 suggest that although the 3D carousel generates more user action, the slide is better at aiding memory. The zoom-in/out tool was the least favored, whereas the mouseover feature fostered greater engagement among power users. Findings from Study 2, which was conducted with a different content domain, replicated the positive effects of slide and negative effects of drag in influencing user experience. Path analyses, using structural equation modeling, revealed the importance of users' assessment of the interface (perceived levels of natural mapping, intuitiveness, and ease of use), which can have significant consequences for user engagement as well as resulting attitudes and behavioral outcomes. Design insights, theories, and techniques to test and capture user experience are discussed.

Visual perception is dependent upon one's physical state. The apparent inclination of a hill is overestimated when the observer is carrying a heavy backpack. But, what if the hill is a virtual one and the user is about to navigate the virtual environment through an avatar? In a 2 (user with a backpack vs. user without the backpack) × 2 (avatar with a virtual backpack vs. avatar without a virtual backpack) × 2 (customized avatar vs. assigned avatar) between-subjects experiment (N = 121), participants estimated the hill as being steeper when using a customized avatar rather than an assigned one. When the avatar is encumbered by a heavy virtual backpack, those with a customized avatar perceived the virtual hill as being more difficult to climb. Avatar customization and the physical resources of the avatar (operationalized here in the form of a 'virtual' backpack) were found to be key predictors of embodied perception in virtual environments (VE). This has implications for the design of games and interventions that make use of VEs.

A paradigmatic quality of interactive interfaces is that they allow users to express themselves, thereby converting message receivers into communication sources. We define this quality as Source Interactivity [26, 29], and test its effects on user experience with a field experiment (N=141) of a portal site featuring cosmetic customization, functional customization and blogging (active versus filter). In demonstrating the psychological influence of source-based interactivity on such outcomes as user engagement, sense of agency, sense of community, intrinsic motivation and attitudes toward the interface, we discuss how designers can use them for creating interactive tools for self-expression.

Do individual differences in dispositional behavioral tendencies, such as immersive tendency and need to belong, play a significant role in human-robot interaction? To answer this question, the present study conducted a 2 x 2 between-subjects experiment to examine the effects of immersive tendency (high vs. low) and need to belong (high vs. low) on individuals' perceptions of a social robot. Preliminary data analyses revealed that participants with a higher level of immersive tendency and need to belong showed greater attachment and trust towards the robot, and were more satisfied with their relationship with the robot than participants with a lower level of immersive tendency and need to belong. In addition, participants with a higher level of immersive tendency experienced greater feelings of social presence. Implications of notable findings are discussed.

This study investigates whether the temperature of a robot's hand can affect perceptions of the robot as a companion. Our research empirically analyzes the responses of 39 individuals randomly assigned to one of three conditions: (1) holding a warm robot hand or (2) holding a cold robot hand or (3) not holding a robot hand. The effects of this simulated 'human touch' on HRI were examined in the context of viewing a horror film clip. Results suggest that experiences of physical warmth and handholding increase feelings of friendship and trust toward the robot. However, the discrepancy between the expectation of an actual human touch and the mechanical appearance of a robot could result in negative effects.

A 2 x 2 between-subjects experiment was conducted to examine the effects of the type of artificial agent (robot vs. computer) and the role of the agent (ally vs. enemy) on people's perceptions and evaluations of the agent when playing a video game. Participants perceived that playing the game with a robot was more enjoyable and easier than playing with a computer. Regardless of the agent type, participants reported that playing the game was more enjoyable when the agent played as an ally rather than as their opponent. Implications of notable findings are discussed.

Advances in the field of the Internet of Things (IoT) have made it possible for everyday objects to attain agency. However, it is unclear how laypersons perceive the increasingly active artifacts. These perceptions are likely to foreground their future responses to IoT objects as they become relevant actors in the physical world and begin to influence everyday user experience. We conducted an in-depth interview study to investigate individuals' knowledge, attitudes, expectations and concerns relating to IoT technologies. Findings show that affordances such as interactivity and modality can be reconceptualized in order to enhance user perceptions of relatedness with the objects. Different from technology-centric and user-centric approaches, the paper suggests a balance between human agency and object agency by adopting a need-oriented design paradigm when building an integral, self-adjusting, user-relevant archetype of IoT.

Online health message boards have become popular, as users not only gain information from other users but also share their own experiences. However, as with most venues of user-generated content, there is need to constantly make quality evaluations as one sifts through enormous amounts of content. Can interface cues, conveying (1) pedigree of users posting content and (2) popularity of the posted content, help new users efficiently make credibility assessments? Furthermore, can the assignment of these same cues to their own posts serve to motivate content generation on their part? These questions were investigated in a 2-session between-subjects experiment (N = 99) with a prototype of a message-board that experimentally varied interface cues, and found that popularity indicators are more influential than pedigree indicators for both evaluation of existing content and contribution of new content. Findings also suggest theoretical mechanisms - involving such concepts as perceived authority, bandwagon effects, sense of agency and sense of community - by which cues affect user experience, providing rich implications for designing and deploying interface cues.

Customization - an attribute that lets users take control and make changes to the presentation and functionality of the interface - is becoming a hallmark of today's interactive media devices. What do users experience when they change interface aspects like fonts and colors, skins on mobile phones, speed dial numbers, privacy settings on social networks and different command menus in software? Do they feel in control? Do they see the customized interface as a reflection of who they are? More importantly, is the feeling of being in control a major driver of usage, or does sense of identity - a personal connection with the interface - prove more vital? This paper discusses the psychology of customization, reports an empirical user study designed to explore the relationship between customization, sense of control, and sense of identity, and outlines implications for design of customizable interfaces based on the findings.

This study examines the differential effects of hedonic vs. utilitarian robots, using a between-subjects experimental design, whereby 48 college students in Korea were randomly assigned to interact with either a Pleo (Dinosaur robot) or a Roomba (vacuum-cleaning robot). Results revealed that hedonic robot (HR) users perceived more enjoyment than utilitarian robot (UR) users, whereas UR users perceived more usefulness and ease-of-use than HR users. Users with high tendency for parasocial interaction (PSI) and high anthropomorphism had more positive attitudes towards robots than their counterparts with low levels of these traits. HR users with high anthropomorphism and PSI had the most positive attitudes than all other combinations of variables. These results indicate that individual differences play a significant moderating role on user attitudes toward hedonic and utilitarian robots. The results of this study suggest that robot developers and marketers should take seriously the labeling of robots as hedonic or utilitarian, and also consider users' individual differences in order to maximize benefits of human-robot interactions.

When a robot is said to be a specialist in a particular domain, does it alter the nature and quality of human-robot interaction? This study examines the effects of specialization in robot functions, along with individual difference in immersive tendencies, on users' trust, perception, activity, and memory. In a controlled experiment, 38 participants were taught a physical exercise lesson from either a specialist or generalist humanoid robot for 6 min. Results showed that specialization had effects on the participants' affective trust; and immersive tendency predicted active participation in the interaction and led to better memory. The latter also moderated the effect of the former - users with higher immersive tendency are more likely to make human attributions of specialization, and rate a specialist robot as more intelligent than a generalist robot. These results have theoretical implications for media-equation as well as design implications for human-robot interaction professionals.

This study explores how human users respond to feedback and evaluation from a robot. A between-subjects experiment was conducted using the Wizard of Oz method, with 63 participants randomly assigned to one of three evaluations (good evaluation vs. neutral evaluation vs. bad evaluation) following a training session. When participants attempted to reproduce the physical motion taught by the robot, they were given a verbal evaluation of their performance by the robot. They showed a strong negative response to the robot when it gave a bad evaluation, while showing positive attraction when it gave a good or neutral evaluation. Participants tended to dismiss criticism from the robot and attributed blame to the robot, while claiming credit to themselves when their performance was rated positively. These results have theoretical implications for the psychology of self-serving bias and practical implications for designing and deploying trainer robots as well as conducting user studies of such robots.

From video games to virtual worlds on the World Wide Web, modern media are becoming increasingly spatial, with users traversing artificial spaces and experiencing a kind of immersion known as “spatial presence.” But how do these media induce spatial presence? Are the affordances for movement provided by these technologies responsible for this illusion? Or do narratives that accompany them persuade us to suspend disbelief and transport ourselves into a virtual space? We explore these theoretical questions by pitting the navigability affordances of a video game against narrative transportation and examining their relative contributions to the formation of spatial presence in a virtual reality context. Results from a large experiment (N = 240) reveal that the narrative actually detracts from spatial presence while traversibility (in the form of greater degrees of steering motion) enhances it even without invoking a mental model of the portrayed environment. Theoretical and practical implications are discussed.

Interactivity has become ubiquitous in the digital media landscape. Numerous interactive tools are designed, tested, deployed and evaluated. Yet, we do not have generalizable knowledge about the larger concept of interactivity and its psychological impact on user experience. As a first step toward a theory of interface interactivity, this paper identifies three species of interactivity corresponding to three central elements of communication - source, medium, and message. Interactivity situated in any of these three loci of communication can provide cues and affordances that operate either individually or together to capture users' attention and determine the nature and depth of their processing of online content as well as contribute to their perceptions, attitudes and behavioral intentions. This paper discusses psychological mechanisms by which the three classes of interactivity tools affect users, with the specific purpose of drawing out design implications and outlining UI challenges for strategic development of interactive interfaces.

This workshop will address the impact of robot morphology on HRI from the perspective of Interaction Science, which encompasses theory and design of human interaction with technology. Anthropomorphic designs, which are common, have to be balanced with the "uncanny valley effect," since different morphologies suggest different affordances to users, triggering a variety of cognitive heuristics and thereby shaping their interactions with robots. We expect progress towards more human-acceptable interactions with robots by understanding the cognitive, behavioral, organizational, and contextual factors of morphology in HRI, as well as new meta-theories and design guidelines. We emphasize a highly multi-disciplinary approach, by involving participants from social sciences, engineering, and design.

Abstract Google News and other newsbots have automated the process of news selection, providing Internet users with a virtually limitless array of news and public information dynamically culled from thousands of news organizations all over the world. In order to help users cope with the resultant overload of information, news leads are typically accompanied by three cues: (a) the name of the primary source from which the headline and lead were borrowed, (b) the time elapsed since the story broke, and (c) the number of related articles written about this story by other news organizations tracked by the newsbot. This article investigates the psychological significance of these cues by positing that the information scent transmitted by each cue triggers a distinct heuristic (mental shortcut) that tends to influence online users' perceptions of a given news item, with implications for their assessment of the item's relevance to their information needs and interests. A large 2 × 3 × 6 within‐subjects online experiment (N = 523) systematically varied two levels of the source credibility cue, three levels of the upload recency cue and six levels of the number‐of‐related‐articles cue in an effort to investigate their effects upon perceived message credibility, newsworthiness, and likelihood of clicking on the news lead. Results showed evidence for source primacy effect, and some indication of a cue‐cumulation effect when source credibility is low. Findings are discussed in the context of machine and bandwagon heuristics.

The psychological tendency to behave socially with a computer is quite well documented in the literature. But does the short-term socialness of human-computer interaction extend over to long-term social relationships with computers? In particular, do we show loyalty to particular computer terminals over a period of time? An electronic observation of campus computer laboratories provided an affirmative answer, following which a survey of computer lab users was conducted to understand theoretical mechanisms governing self-reported loyalty to computers. In particular, it explored whether individual differences relating to psychological anthropomorphism and/or preference for consistency played a role in human loyalty toward computers. Results indicate that anthropomorphism more strongly predicts ‘hardcore loyalty’ to computers whereas consistency is more strongly associated with ‘reinforcing loyalty.’ This paper discusses theoretical and practical implications of these findings.