Visualizations often encode multivariate data by mapping attributes to distinct visual channels such as color, size, or shape. The effectiveness of these encodings depends on separability--the extent to which channels can be perceived independently. Yet systematic evidence for separability, especially in map-based contexts, is lacking. We present a crowdsourced experiment that evaluates the separability of four channel pairs--color (ordered) x shape, color (ordered) x size, size x shape, and size x orientation--in the context of bivariate symbol maps. Both accuracy and speed analyses show that color x shape is the most separable and size x orientation the least separable, while size x color and size x shape do not differ. Separability also proved asymmetric--performance depended on which channel encoded the task-relevant variable, with color and shape outperforming size, and square shape especially difficult to discriminate. Our findings advance the empirical understanding of visual separability, with implications for multivariate map design.

Responsiveness in large language model (LLM) applications is widely assumed to be critical, yet the impact of latency on user behavior and perception of output quality has not been systematically explored. We report a controlled experiment varying time-to-first-token latency (2, 9, 20 seconds) across two taxonomy-driven knowledge task types (Creation and Advice). Log analyses reveal that user interaction behaviors were robust to latency, yet varied by task type: Creation tasks elicited more frequent prompting than Advice tasks. In contrast, participants who experienced 2-second latencies rated the LLM’s outputs less thoughtful and useful than those who experienced 9- or 20-second latencies. Participants attributed delays to AI deliberation, though long waits occasionally shifted this interpretation toward frustration or concerns about reliability. Overall, this work demonstrates that latency is not simply a cost to reduce but a tunable design variable with ethical implications. We offer design strategies for enhancing human-LLM interaction.

System-imposed wait times can significantly disrupt digital workflows, affecting user experience and task performance. Prior HCI research has examined how temporal feedback, such as feedback mode (Elapsed-Time vs. Remaining-Time) shapes wait-time perception. However, few studies have investigated how such feedback influences users’ downstream task performance, as well as overall affective and cognitive experience. To study these effects, we conducted an online experiment where 425 participants performing a visual reasoning task experienced a 10-, 30-, or 60-second wait with a Remaining-Time, Elapsed-Time, or No Time Display. Findings show that temporal feedback mode shapes how waiting is perceived: Remaining-Time feedback increased frustration relative to Elapsed-Time feedback, while No Time Display made waits feel longer and heightened ambiguity. Notably, these experiential differences did not translate into differences in post-wait task performance. Integrating psychophysical and cognitive science perspectives, we discuss implications for implementing temporal feedback in latency-prone digital systems.

This study investigates how professional writers’ complex relationship with GenAI shapes their work practices and outcomes. Through a cross-sectional survey with writing professionals (n=403) in diverse roles, we show that collaboration and rivalry orientation are associated with differences in work practices and outcomes. Rivalry is primarily associated with relational crafting and skill maintenance. Collaboration is primarily associated with task crafting, productivity, and satisfaction, at the cost of long-term skill deterioration. Combination of the orientations (high rivalry and high collaboration) reconciles these differences, while boosting the association with the outcomes. Our findings argue for a balanced approach where high levels of rivalry and collaboration are essential to shape work practices and generate outcomes aimed at the long-term success of the job. We present key design implications on how to increase friction (rivalry) and reduce over-reliance (collaboration) to achieve a more balanced relationship with GenAI.

Generative AI (GAI) technologies are disrupting professional writing, challenging traditional practices. Recent studies explore GAI adoption experiences of creative practitioners, but we know little about how these experiences evolve into established practices and how GAI resistance alters these practices. To address this gap, we conducted 25 semi-structured interviews with writing professionals who adopted and/or resisted GAI. Using the theoretical lens of Job Crafting, we identify four strategies professionals employ to reshape their roles. Writing professionals employed GAI resisting strategies to maximize human potential, reinforce professional identity, carve out a professional niche, and preserve credibility within their networks. In contrast, GAI-enabled strategies allowed writers who embraced GAI to enhance desirable workflows, minimize mundane tasks, and engage in new AI-managerial labor. These strategies amplified their collaborations with GAI while reducing their reliance on other people. We conclude by discussing implications of GAI practices on writers' identity and practices as well as crafting theory.

Human advisors increasingly have access to AI recommendations and use them to shape the advice they give clients, often without disclosing their AI use to their clients. Disclosure of AI use involves informing individuals when AI is being used on their behalf by another person, such as disclosing to clients that advisors are using AI to formulate their expert advice. Our study aims to investigate whether and how disclosing advisors' use of AI assistance to their clients influences the advisors' perceived responsibility for decisions and degree of AI reliance. Recruiting financial advisors to perform a personal finance advising simulation that manipulated whether clients would know that advisors used an AI system (disclosed vs. not disclosed), we found that financial advisors felt less responsible for their recommendations when they believed their use of AI assistance would be disclosed rather than undisclosed to clients. We also found that advisors' perceived personal responsibility was higher when their reliance on AI was lower. Advisors' perceived self-competence increased their perceived personal responsibility for investment decisions relative to AI, and their trust in AI decreased their perceived responsibility. We conclude by discussing how our findings can inform disclosure schemes for improved human-AI collaboration in advising.

We report on design-focused inquiry into future multiple sclerosis (MS) healthcare; including a multi-stage design process with experienced MS clinicians, and formative evaluations with people living with MS. MS is a chronic, progressive, and unpredictable inflammatory neurological disease of the central nervous system that affects at least 2.8 million people worldwide. Walking impairments affect up to 85% of people diagnosed with MS. Responding to this, our focus is on design for longitudinally monitoring mobility, and in particular using wearable sensors that generate data on gait metrics to support clinical and self-care decision-making. We contribute to HCI research in three ways: (1) a detailed case study design process, including artifacts; (2) metaphorical framing concepts, with associated use cases illustrated through design scenarios; and (3) understanding of virtual-first practices in rehabilitation medicine that can be translated beyond MS care.

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.

Homophily refers to the tendency of individuals to associate with others who are similar to them in characteristics, such as, race, ethnicity, age, gender, or interests. In this paper, we investigate if individuals exhibit racial homophily when viewing visualizations, using mass shooting data in the United States as the example topic. We conducted a crowdsourced experiment (N=450) where each participant was shown a visualization displaying the counts of mass shooting victims, highlighting the counts for one of three racial groups (White, Black, or Hispanic). Participants were assigned to view visualizations highlighting their own race or a different race to assess the influence of racial concordance on changes in affect (emotion) and attitude towards gun control. While we did not find evidence of homophily, the results showed a significant negative shift in affect across all visualization conditions. Notably, political ideology significantly impacted changes in affect, with more liberal views correlating with a more negative affect change. Our findings underscore the complexity of reactions to mass shooting visualizations and suggest that future research should consider various methodological improvements to better assess homophily effects.

Remote Patient Monitoring (RPM) devices transmit patients' medical indicators (e.g., blood pressure) from the patient's home testing equipment to their healthcare providers, in order to monitor chronic conditions such as hypertension. AI systems have the potential to enhance access to timely medical advice based on the data that RPM devices produce. In this paper, we report on three studies investigating how the severity of users' medical condition (normal vs. high blood pressure), security risk (low vs. modest vs. high risk), and medical advice source (human doctor vs. AI) influence user perceptions of advisor trustworthiness and willingness to disclose RPM-acquired information. We found that trust mediated the relationship between the advice source and users' willingness to disclose health information: users trust doctors more than AI and are more willing to disclose their RPM-acquired health information to a more trusted advice source. However, we unexpectedly discovered that conditional on trust, users disclose RPM-acquired information more readily to AI than to doctors. We observed that the advice source did not influence perceptions of security and privacy risks. We conclude by discussing how our findings can support the design of RPM applications.

Open data programs have become increasingly established at national and local levels of government. While the degree of success these programs have had in achieving their objectives remains open to question, one factor that has been identified as important to any success is the role of open data intermediaries, individuals and organizations that help others to make use of open data. In this paper we investigate how people become engaged with open data, what their motivations are, and the barriers and facilitators program participants perceive with regard to using open data effectively. We interview participants from a variety of backgrounds with differing levels of experience and engagement with open data. Participants include students learning how to train others in open data techniques and tools; people who attend open data events and use open data for commercial or social benefit; and representatives from local government, municipal agencies and a civic tech non-profit. We identify pathways to successfully developing and nurturing a community of open data intermediaries, and make five recommendations for organizations planning and managing open data programs.

Walking impairment is a debilitating symptom of Multiple Sclerosis (MS), a disease affecting 2.8 million people worldwide. While clinicians’ in-person observational gait assessments are important, research suggests that data from wearable sensors can indicate early onset of gait impairment, track patients’ responses to treatment, and support remote and longitudinal assessment. We present an inquiry into supporting the transition from research to clinical practice. Co-design by HCI, biomedical, neurology and rehabilitation researchers resulted in a data-rich interface prototype for augmented gait analysis based on visualized sensor data. We used this as a prompt in interviews with ten experienced clinicians from a range of MS rehabilitation roles. We find that clinicians value quantitative sensor data within a whole patient narrative, to help track specific rehabilitation goals, but identify a tension between grasping critical information quickly and more detailed understanding. Based on the findings we make design recommendations for data-rich remote rehabilitation interfaces.

Noise pollution is among the most consistently cited and highest impact quality-of-life issues in major urban areas across the US, with more than 70 million people estimated to be exposed to noise levels considered harmful. While HCI and CSCW has a relatively rich history of engagement with monitoring such environmental concerns, e.g. through participatory sensing, prior research has not to our knowledge engaged with the process of municipal mitigation. In this paper we present research in this direction, connecting support for pervasive environmental monitoring with civic engagement in mitigation action. Having first identified and described the research space for this engagement, we present empirical data from an ongoing case study focused on two communities living with chronic problem noise. We probe the experiences of residents and representatives of different municipal organizations tasked with addressing their concerns. We find that making and drawing on records of noise is important to residents and authorities, that these groups have misaligned perceptions of how effective current reporting programs are, and that communication between them can be poor. We also find that noise is often one part of more complex issues. We then discuss our findings in light of prior research, and identify a model of civic sensing that highlights opportunities for HCI design and research including: supporting residents' coordinated actions and actions with municipal open data, mediating residents' and authorities' assessments of data quality, and supporting accountability and attributable mitigation action.

This paper investigates the tools and practices used by Orientation and Mobility (O&M) specialists in instructing people who are blind or have low vision in concepts, skills, and techniques for safe and independent travel. Based on interviews with experienced instructors who practice in different O&M settings we find that a shortage of qualified specialists and restrictions on in-person activities during COVID-19 has accelerated interest in remote instruction and assessment, while widespread adoption of smartphones with accessibility support has driven interest in assistive apps. This presents both opportunities and challenges for a practice that is traditionally conducted in-person and assessed through qualitative observations. In response we identify multiple opportunities for HCI research in service of O&M, including: supporting a 'physician's assistant' model of remote O&M instruction and assessment, matching O&M instructors' clients with guide dogs, highlighting clients' progress towards O&M goals, and collaboratively planning routes and monitoring clients' independent travel progress.

In this work we explore confidence elicitation methods for crowdsourcing "soft" labels, e.g., probability estimates, to reduce the annotation costs for domains with ambiguous data. Machine learning research has shown that such "soft" labels are more informative and can reduce the data requirements when training supervised machine learning models. By reducing the number of required labels, we can reduce the costs of slow annotation processes such as audio annotation. In our experiments we evaluated three confidence elicitation methods: 1) "No Confidence" elicitation, 2) "Simple Confidence" elicitation, and 3) "Betting" mechanism for confidence elicitation, at both individual (i.e., per participant) and aggregate (i.e., crowd) levels. In addition, we evaluated the interaction between confidence elicitation methods, annotation types (binary, probability, and z-score derived probability), and "soft" versus "hard" (i.e., binarized) aggregate labels. Our results show that both confidence elicitation mechanisms result in higher annotation quality than the "No Confidence" mechanism for binary annotations at both participant and recording levels. In addition, when aggregating labels at the recording level, results indicate that we can achieve comparable results to those with 10-participant aggregate annotations using fewer annotators if we aggregate "soft" labels instead of "hard" labels. These results suggest that for binary audio annotation using a confidence elicitation mechanism and aggregating continuous labels we can obtain higher annotation quality, more informative labels, with quality differences more pronounced with fewer participants. Finally, we propose a way of integrating these confidence elicitation methods into a two-stage, multi-label annotation pipeline.

OBJECTIVE: Through the coronavirus disease 2019 (COVID-19) pandemic, telemedicine became a necessary entry point into the process of diagnosis, triage, and treatment. Racial and ethnic disparities in healthcare have been well documented in COVID-19 with respect to risk of infection and in-hospital outcomes once admitted, and here we assess disparities in those who access healthcare via telemedicine for COVID-19. MATERIALS AND METHODS: Electronic health record data of patients at New York University Langone Health between March 19th and April 30, 2020 were used to conduct descriptive and multilevel regression analyses with respect to visit type (telemedicine or in-person), suspected COVID diagnosis, and COVID test results. RESULTS: Controlling for individual and community-level attributes, Black patients had 0.6 times the adjusted odds (95% CI: 0.58-0.63) of accessing care through telemedicine compared to white patients, though they are increasingly accessing telemedicine for urgent care, driven by a younger and female population. COVID diagnoses were significantly more likely for Black versus white telemedicine patients. DISCUSSION: There are disparities for Black patients accessing telemedicine, however increased uptake by young, female Black patients. Mean income and decreased mean household size of a zip code were also significantly related to telemedicine use. CONCLUSION: Telemedicine access disparities reflect those in in-person healthcare access. Roots of disparate use are complex and reflect individual, community, and structural factors, including their intersection-many of which are due to systemic racism. Evidence regarding disparities that manifest through telemedicine can be used to inform tool design and systemic efforts to promote digital health equity.

This study provides data on the feasibility and impact of video-enabled telemedicine use among patients and providers and its impact on urgent and nonurgent healthcare delivery from one large health system (NYU Langone Health) at the epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States. Between March 2nd and April 14th 2020, telemedicine visits increased from 102.4 daily to 801.6 daily. (683% increase) in urgent care after the system-wide expansion of virtual urgent care staff in response to COVID-19. Of all virtual visits post expansion, 56.2% and 17.6% urgent and nonurgent visits, respectively, were COVID-19-related. Telemedicine usage was highest by patients 20 to 44 years of age, particularly for urgent care. The COVID-19 pandemic has driven rapid expansion of telemedicine use for urgent care and nonurgent care visits beyond baseline periods. This reflects an important change in telemedicine that other institutions facing the COVID-19 pandemic should anticipate.

Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity. When designing these applications, is it better to offer: a) advice that if strictly adhered to is more likely to result in an individual successfully achieving their goal, even if fewer users will choose to adopt it? or b) advice that is likely to be adopted by a larger number of users, but which is sub-optimal with regard to any particular individual achieving their goal? We identify this dilemma, characterized as Goal-Directed vs. Adoption-Directed advice, and investigate the design questions it raises through an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. We report findings that suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications.

Annotating rich audio data is an essential aspect of training and evaluating machine listening systems. We approach this task in the context of temporally-complex urban soundscapes, which require multiple labels to identify overlapping sound sources. Typically this work is crowdsourced, and previous studies have shown that workers can quickly label audio with binary annotation for single classes. However, this approach can be difficult to scale when multiple passes with different focus classes are required to annotate data with multiple labels. In citizen science, where tasks are often image-based, annotation efforts typically label multiple classes simultaneously in a single pass. This paper describes our data collection on the Zooniverse citizen science platform, comparing the efficiencies of different audio annotation strategies. We compared multiple-pass binary annotation, single-pass multi-label annotation, and a hybrid approach: hierarchical multi-pass multi-label annotation. We discuss our findings, which support using multi-label annotation, with reference to volunteer citizen scientists' motivations.

As technology advances, people increasingly interact with virtual objects in settings such as augmented reality (AR) where the virtual layer is superimposed on top of the physical world. Similarly to interactions with physical objects, users may assign virtual objects with value, experience a sense of relatedness, and develop psychological ownership over these objects. The objective of this study is to understand how AR's unique characteristics influences the emergence of meaning and ownership perceptions amongst users. We conducted a study of users' interactions with a virtual dog over a three-week period, comparing AR and fully virtual settings. Our findings show that engagement with the application is a key determinant of the relation users develop with virtual objects. However, the effect of the background layer-whether physical or virtual-dominates the development of relatedness and ownership feelings, highlighting the importance of the "real" physical layer in shaping users' perceptions.

Advancements in computer‐mediated exercise put forward the feasibility of telerehabilitation, but it remains a challenge to retain patients' engagement in exercises. Building on our previous study demonstrating enhanced engagement in citizen science through social information about others' contributions, we propose a novel framework for effective telerehabilitation that integrates citizen science and social information into physical exercise. We hypothesized that social information about others' contributions would augment engagement in physical activity by encouraging people to invest more effort toward discovery of novel information in a citizen science context. We recruited healthy participants to monitor the environment of a polluted canal by tagging images using a haptic device toward gathering environmental information. Along with the images, we displayed the locations of the tags created by the previous participants. We found that participants increased both the amount and duration of physical activity when presented with a larger number of the previous tags. Further, they increased the diversity of tagged objects by avoiding the locations tagged by the previous participants, thereby generating richer information about the environment. Our results suggest that social information is a viable means to augment engagement in rehabilitation exercise by incentivizing the contribution to scientific activities.

Recent years are seeing a sharp increase in the availability of personal omic (e.g. genomes, microbiomes) data to non-experts through direct-to-consumer testing kits. While the scientific understanding of human -omic information is evolving, the interpretation of the data may impact well-being of users and relevant others, and therefore poses challenges and opportunities for CSCW research. We identify the information, interaction, and sense-making needs of microbiomic data users, within the broader context of social omics - the sharing and collaborative engagement with data and interpretation. Analyzing users' discussions on Reddit's r/HumanMicrobiome, we identified seven user needs for microbiome data: reviewing an annotated report, comparing microbiome data, tracking changes, receiving personalized actionable information, curating and securing information, documenting and sharing self experiments, and enhancing the communication between patients and health-care providers. We highlight the ways in which users interact with each other to collaboratively make sense of the data. We conclude with design implications, including tools for better communication with care providers, and for symptom-centered sharing and discussion.

We present Cicero, a mixed-initiative application for prototyping two-dimensional sprite-based games across different genres such as shooters, puzzles, and action games. Cicero provides a host of features which can offer assistance in different stages of the game development process. Noteworthy features include AI agents for gameplay simulation, a game mechanics recommender system, a playtrace aggregator, heatmap-based game analysis, a sequential replay mechanism, and a query system that allows searching for particular interaction patterns. In order to evaluate the efficacy and usefulness of the different features of Cicero, we conducted a user study in which we compared how users perform in game debugging tasks with different kinds of assistance.

How valuable are certain interface features to their users? How can users' demand for features be quantified? To address these questions, users' demand curve for the sorting feature was elicited in a controlled experiment, using personal finance as the user context. Users made ten rounds of investment allocation across up to 77 possible funds, thus encountering choice overload, typical of many online environments. Users were rewarded for positive investment returns. To overcome choice overload, users could sort the alternatives based on product attributes (fees, category, fund name, past performance). To elicit their demand for sorting, the experimental design enabled users to forgo 0%-9% of their reward in return for activating the sorting feature. The elicited downward sloping demand curve suggests a curvilinear relationship between sorting use and cost. More broadly, the study offers a way to quantify user demand of UI features, and a basis for comparison between features.

Audio annotation is an important step in developing machine-listening systems. It is also a time consuming process, which has motivated investigators to crowdsource audio annotations. However, there are many factors that affect annotations, many of which have not been adequately investigated. In previous work, we investigated the effects of visualization aids and sound scene complexity on the quality of crowdsourced sound-event annotations. In this paper, we extend that work by investigating the effect of sound-event loudness on both sound-event source annotations and sound-event proximity annotations. We find that the sound class, loudness, and annotator bias affect how listeners annotate proximity. We also find that loudness affects recall more than precision and that the strengths of these effects are strongly influenced by the sound class. These findings are not only important for designing effective audio annotation processes, but also for effectively training and evaluating machine-listening systems.

Maintaining participation beyond the initial period of engagement is critical for peer production systems. Theory suggests that an increase in motivation is expected with contributors' movement from the community periphery to the core. Less is known, however, about how specific motivations change over time. We fill this gap by focusing on individual motivational paths in the formative periods of engagement, exploring which motivations change and how. We collected data on various instrumental and non-instrumental motivations at two points in study participants? Wikipedia career: when they started editing and again after six months. We found that non-instrumental motivations (including collective and intrinsic motives) decreased significantly over time, in contrast with socially-driven motivations such as norm-oriented motivates which did not change and social motives which increased marginally. The findings offer new insights into newcomers' evolving motivations, with implications for designing and managing peer-production systems.

We investigate the impact of using anthropomorphized data graphics over standard charts on viewers' empathy for, and prosocial behavior toward suffering populations, in the context of human rights narratives. We present a series of experiments conducted on Amazon Mechanical Turk, in which we compare various forms of anthropomorphized data graphics-ranging from a single human figure that "fills up" to show proportional data, to separated groups of individual human beings-with a standard chart baseline. Each experiment uses two carefully crafted human rights data-driven stories to present the graphics. Contrary to our expectations, we consistently find that anthropomorphized data graphics and standard charts have very similar effects on empathy and prosocial behavior.

Research on peer-production suggests that as participants choose what actions to perform, prototypical activity patterns emerge. Recent work characterized these patterns and demonstrated that informal emergent roles are highly stable. Nonetheless, we know little about the ways in which contributors take on and shed emergent roles. The objectives of this study are to: (a) delineate the temporal dynamics of participants' emergent role taking behaviors, and (b) identify the motivations driving role-transition behaviors. Our study links motivation to role-transition behaviors within Wikipedia. Our first sample covered eleven years and 222,119 contributors, and was used to identify four categories of temporal role-taking behaviors, that differ in their mobility between emergent roles and across Wikipedia articles. Our second examination linked the motivations of 175 new participants to their subsequent role-taking activity over 14 months. Together, the two analyses reveal that role-taking categories can be distinguished based on participants' motivational orientation (intrinsic/extrinsic and self/others-oriented).

Financial prospectuses, which are available to consumers who buy financial products, are intended to help inform decision-making. While prospectuses provide a wealth of information, they are complex and difficult to understand for the vast majority of their intended readers. To help non-experts make informed decisions, we investigated how social annotations with comparative statements embedded into online prospectuses influence users' decisions and perceptions about decisions. We recruited 31 pre-study users to annotate 10 retirement saving plan prospectuses. We then embedded these annotations in prospectuses provided to another set of 228 users (147 novices and 81 experts) in a 35-period retirement saving simulation. Novices benefited from exposure to social annotations, and were more likely to meet their retirement saving goals than those not exposed to annotations. Exposure to social annotations brought novices' performance level to that of experts, but at the same time, led them to lower perceived understanding of the prospectus.

Product information labels can help users understand complex information, leading them to make better decisions. One area where consumers are particularly prone to make costly decision‐making errors is long‐term saving, which requires understanding of complex concepts such as uncertainty and trade‐offs. Although most people are poorly equipped to deal with such concepts, interactive design can potentially help users make better decisions. We developed an interactive information label to assist consumers with retirement saving decision‐making. To evaluate it, we exposed 450 users to one of four user interface conditions in a retirement saving simulator where they made 35 yearly decisions under changing circumstances. We found a significantly better ability of users to reach their goals with the information label. Furthermore, users who interacted with the label made better decisions than those who were presented with a static information label. Lastly, we found the label particularly effective in helping novice savers.

Online participation is becoming an increasingly common means for individuals to contribute to citizen science projects, yet such projects often rely on only a small fraction of participants to make the majority of contributions. Here, we investigate a means for influencing the performance of citizen scientists toward enhancing overall participation. Building on past social comparison research, we pair citizen scientists with a software‐based virtual peer in an environmental monitoring project. Through a series of experiments in which virtual peers outperform, underperform, or perform similarly to human participants, we investigate the influence of their presence on citizen science participation. To offer insight into the psychological determinants to the response to this intervention, we propose a new dynamic model describing the bidirectional interaction between humans and virtual peers. Our results demonstrate that participant contribution can be enhanced through the presence of a virtual peer, creating a feedback loop where participants tend to increase or decrease their contribution in response to their peers' performance. By including virtual peers that systematically outperform the participants, we demonstrate a fourfold increase in their contribution to the citizen science project.

Peer production communities often struggle to retain contributors beyond initial engagement. This may be a result of contributors' level of motivation, as it is deeply intertwined with activity. Existing studies on participation focus on activity dynamics but overlook the accompanied changes in motivation. To fill this gap, this study examines the interplay between contributors' fun motives and activity over time. We combine motivational data from two surveys of Wikipedia newcomers with data of two periods of editing activity. We find that persistence in editing is related to fun, while the amount of editing is not: individuals who persist in editing are characterized by higher fun motives early on (when compared to dropouts), though their motives are not related to the number of edits made. Moreover, we found that newcomers' experience of fun was reinforced by their amount of activity over time: editors who were initially motivated by fun entered a virtuous cycle, whereas those who initially had low fun motives entered a vicious cycle. Our findings shed new light on the importance of early experiences and reveal that the relationship between motivation and participation levels is more complex than previously understood.

Audio annotation is key to developing machine-listening systems; yet, effective ways to accurately and rapidly obtain crowdsourced audio annotations is understudied. In this work, we seek to quantify the reliability/redundancy trade-off in crowdsourced soundscape annotation, investigate how visualizations affect accuracy and efficiency, and characterize how performance varies as a function of audio characteristics. Using a controlled experiment, we varied sound visualizations and the complexity of soundscapes presented to human annotators. Results show that more complex audio scenes result in lower annotator agreement, and spectrogram visualizations are superior in producing higher quality annotations at lower cost of time and human labor. We also found recall is more affected than precision by soundscape complexity, and mistakes can be often attributed to certain sound event characteristics. These findings have implications not only for how we should design annotation tasks and interfaces for audio data, but also how we train and evaluate machine-listening systems.

The increase in the availability of personal genomic data to lay consumers using online services poses a challenge to HCI researchers: such data are complex and sensitive, involve multiple dimensions of uncertainty, and can have substantial implications for individuals' well-being. Personal genomic data are also unique because unlike other personal data, which constantly change, genomic data are largely stable during a person's lifetime; it is their interpretation and implications that change over time as new medical research exposes relationships between genes and health. In this paper, we present a novel tool for self exploration of personal genomic data. To evaluate the usability and utility of the tool, we conducted the first study of a genome interpretation tool to date, in which users used their own personal genomic data. We conclude by offering design implications for the development of interactive personal genomic reports.

In this study we explore the impact of exposure to social annotation, embedded in online consent forms, on individuals' beliefs and decisions in the context of informed consent. In this controlled between-subjects experiment, participants were presented with an online consent form for a personal genomics study. Individuals were randomly assigned to either a social annotation condition that exposed them to previous users' comments on-screen, or to a traditional consent form without social input. We compared participants' perceptions about their consent decision, their trust in the organization seeking the consent, and their actual consent across conditions. While no significant difference was observed between actual consent rates, we found that on average individuals exposed to social annotation felt that their decision was more informed, and furthermore, that the effect of the exposure to social annotation was stronger among users characterized by relatively lower levels of prior privacy preserving behaviors.

One of the key challenges for innovation and technology‐mediated knowledge collaboration within organizational settings is motivating contributors to share their knowledge. Drawing upon self‐determination theory, we investigate 2 forms of motivation: internally driven (autonomous motivation) and externally driven (controlled motivation). Knowledge sharing could be viewed as a required in‐role activity or as discretionary extra‐role behavior. In this study, we examine the moderating effect of role perceptions on the relations between each of the two motivational constructs and knowledge sharing, paying particular attention to the affordances of the enabling information technology. An analysis of survey data from a wiki‐based organizational encyclopedia in a large, multinational firm reveals that when contributors' motivation is externally driven, they are more likely to share knowledge if this activity is viewed as in‐role behavior. However, when contributors' motivation is internally driven, they are more likely to participate in knowledge sharing when this activity is viewed as extra‐role behavior. Theoretical and practical implications are discussed.

Crowdsourcing has seen a substantial increase in interest from researchers and practitioners in recent years. Being a new form of work facilitated by information technology, the rise of crowdsourcing calls for the development of new theoretical insights. Our focus in this article is on extra‐role behavior—employees' voluntary activities, which are not part of their prescribed duties. Specifically, we explored how user interface design can help increase extra‐role behavior among crowdsourcing workers. In a randomized experiment, we examined the joint effects of the presentation of a performance display to crowdsourcing workers and the personal attributes of these workers on the workers' likelihood to engage in extra‐role behavior. The experimental setting included an image analysis task performed on an environmental monitoring website. We compared workers' behavior across the different experimental conditions and found that the interaction between the presence of a performance display and the workers' personality trait of curiosity has a significant impact on the likelihood of engaging in extra‐role behavior. In particular, the presence of a performance display was associated with increased likelihood of extra‐role behavior among low‐curiosity workers, and no change in extra‐role behavior was observed among high‐curiosity users. Implications for design are discussed.

Can human-computer interaction help people make informed and effective decisions about their retirement savings? We applied the behavioral economic theories of endowment effect and loss aversion to the design of novel retirement saving user interfaces. To examine effectiveness, we conducted an experiment in which 487 participants were exposed to one of three experimental user interface designs of a retirement saving simulator, representing endowment effect, loss aversion and control. Users made 34 yearly asset allocation decisions. We found that designs informed by the endowment effect and loss aversion theories and which communicated to savers the long-term implications of their asset allocation choices, led users to adjust their behavior, make larger and more frequent asset allocation changes, and achieve their saving goals more effectively.

In this paper, we present an empirical analysis of deceptive visualizations. We start with an in-depth analysis of what deception means in the context of data visualization, and categorize deceptive visualizations based on the type of deception they lead to. We identify popular distortion techniques and the type of visualizations those distortions can be applied to, and formalize why deception occurs with those distortions. We create four deceptive visualizations using the selected distortion techniques, and run a crowdsourced user study to identify the deceptiveness of those visualizations. We then present the findings of our study and show how deceptive each of these visual distortion techniques are, and for what kind of questions the misinterpretation occurs. We also analyze individual differences among participants and present the effect of some of those variables on participants' responses. This paper presents a first step in empirically studying deceptive visualizations, and will pave the way for more research in this direction.

An understanding of participation dynamics within online production communities requires an examination of the roles assumed by participants. Recent studies have established that the organizational structure of such communities is not flat; rather, participants can take on a variety of well-defined functional roles. What is the nature of functional roles? How have they evolved? And how do participants assume these functions? Prior studies focused primarily on participants' activities, rather than functional roles. Further, extant conceptualizations of role transitions in production communities, such as the Reader to Leader framework, emphasize a single dimension: organizational power, overlooking distinctions between functions. In contrast, in this paper we empirically study the nature and structure of functional roles within Wikipedia, seeking to validate existing theoretical frameworks. The analysis sheds new light on the nature of functional roles, revealing the intricate "career paths" resulting from participants' role transitions.

In social recommendation systems, users often publicly rate objects such as photos, news articles or consumer products. When they appear in aggregate, these ratings carry social signals such as the direction and strength of the raters' average opinion about the product. Using a controlled experiment we manipulated two central social signals -- the direction and strength of social ratings of five popular consumer products -- and examined their interacting effects on users' ratings. The results show an asymmetric user behavior, where the direction of perceived social rating has a negative effect on users' ratings if the direction of perceived social rating is negative, but no effect if the direction is positive. The strength of perceived social ratings did not have a significant effect on users' ratings. The findings highlight the potential for cascading adverse effects of small number of negative user ratings on subsequent users' opinions.

Data visualization has been used extensively to inform users. However, little research has been done to examine the effects of data visualization in influencing users or in making a message more persuasive. In this study, we present experimental research to fill this gap and present an evidence-based analysis of persuasive visualization. We built on persuasion research from psychology and user interfaces literature in order to explore the persuasive effects of visualization. In this experimental study we define the circumstances under which data visualization can make a message more persuasive, propose hypotheses, and perform quantitative and qualitative analyses on studies conducted to test these hypotheses. We compare visual treatments with data presented through barcharts and linecharts on the one hand, treatments with data presented through tables on the other, and then evaluate their persuasiveness. The findings represent a first step in exploring the effectiveness of persuasive visualization.

We present a theoretical foundation and empirical findings demonstrating the effectiveness of personality-targeted design. Much like a medical treatment applied to a person based on his specific genetic profile, we argue that theory-driven, personality-targeted UI design can be more effective than design applied to the entire population. The empirical exploration focused on two settings, two populations and two personality traits: Study 1 shows that users' extraversion level moderates the relationship between the UI cue of audience size and users' contribution. Study 2 demonstrates that the effectiveness of social anchors in encouraging online contributions depends on users' level of emotional stability. Taken together, the findings demonstrate the potential and robustness of the interactionist approach to UI design. The findings contribute to the HCI community, and in particular to designers of social systems, by providing guidelines to targeted design that can increase online participation.

We introduce a framework for personality-targeted design. Much like a medical treatment applied to a person based on his specific genetic profile, we make the case for theory-driven personalized UI design, and argue that it can be more effective than design applied equally to the entire population. In particular, we show that users' conscientiousness levels determine their reactions to UI indicators of critical mass. We created a simulated social recommender system in which participants answer a short personality questionnaire and are subsequently presented with a picture of a pet that purports to be the "best match" for their personality. We then manipulated the UI by providing indicators of the existence and the lack of critical mass. We tested whether the interaction between personality and UI design affects users' participation. The findings validate our hypothesis, showing that manipulation of the critical mass indicators affect high-conscientiousness and low-conscientiousness participants in opposite directions.

In Wikipedia, volunteers collaboratively author encyclopedic entries, and therefore managing conflict is a key factor in group success. Behavioral research describes 3 conflict types: task‐related, affective, and process. Affective and process conflicts have been consistently found to impede group performance; however, the effect of task conflict is inconsistent. We propose that these inconclusive results are due to underspecification of the task conflict construct, and focus on the transition phase where task‐related disagreements escalate into affective and process conflict. We define these transitional phases as distinct constructs—task‐affective and task‐process conflict—and develop a theoretical model that explains how the various task‐related conflict constructs, together with the composition of the wiki editor group, determine the quality of the collaboratively authored wiki article. Our empirical study of 96 Wikipedia articles involved multiple data‐collection methods, including analysis of Wikipedia system logs, manual content analysis of articles' discussion pages, and a comprehensive assessment of articles' quality using theDelphi method. Our results show that when group members' disagreements—originally task related—escalate into personal attacks or hinge on procedure, these disagreements impede group performance. Implications for research and practice are discussed.

The power-law distribution of participation characterizes a wide variety of technology-mediated social participation (TMSP) systems, and Wikipedia is no exception. A minority of active contributors does most of the work. While the existence of a core of highly active contributors is well documented, how those individuals came to be so active is less well understood. In this study we extend prior research on TMSP and Wikipedia by examining in detail the characteristics of the revisions that new contributors make. In particular we focus on new users who maintain a minimum level of sustained activity during their first six months. We use content analysis of individual revisions as well as other quantitative techniques to examine three research questions regarding the effect of early diversification of activity, nature vs. nurture, and associations with later administrative and organizational activity. We present analyses that address each of these questions, and conclude with implications for our understanding of the progression of participation on Wikipedia and other TMSP systems.

Although electronic medical records (EMR) adoption by health care organizations has been widely studied, little is known about the determinants of EMR individual use by physicians after institutional adoption has taken place. In this study, the determinants of inpatient physicians' continuous use of EMR were studied. Four dimensions of EMR use were analyzed: use intensity, use extent, use frequency, and use scope. A web‐based survey was administered to physicians at a large university hospital; respondents filled out a survey with questions relating to their EMR use, attitude, beliefs, work style, and dispositional resistance to change. Structural equation modeling was carried out to analyze the relationship between these factors. Physicians were found to differ substantially in the scope, extent, and intensity of their EMR use. Their attitude toward EMR use was associated with all use dimensions. Dispositional resistance to change was negatively related to perceived ease of use and with perceived usefulness both directly and through the mediation of compatibility with preferred work style. Time loss was negatively related to both perceived usefulness and attitude toward EMR use. Implications for research and practice are discussed.

Few studies of online communities take exogenous factors into account while explaining community participation. We present preliminary results from a study investigating the impact of steward companies' actions on online community participation. We identified two events: (1) open sourcing of Java by Sun and (2) acquisition of Sun (and consequently of Java) by Oracle, and examined participation in their developer online communities. We found significant change in participation levels around each event with both significant increases and decreases. We conjecture that participation increased if the action was perceived as supportive by developers (e.g. Sun's open sourcing of Java) whereas it decreased if the action was perceived as detrimental by developers (e.g. Oracle's acquisition of Sun).

The success of Wikipedia and the relative high quality of its articles seem to contradict conventional wisdom. Recent studies have begun shedding light on the processes contributing to Wikipedia's success, highlighting the role of coordination and contribution inequality. In this study, we expand on these works in two ways. First, we make a distinction between global (Wikipedia-wide) and local (article-specific) inequality and investigate both constructs. Second, we explore both direct and indirect effects of these inequalities, exposing the intricate relationships between global inequality, local inequality, coordination, and article quality. We tested our hypotheses on a sample of a Wikipedia articles using structural equation modeling and found that global inequality exerts significant positive impact on article quality, while the effect of local inequality is indirect and is mediated by coordination

Volunteer computing is a powerful way to harness distributed resources to perform large-scale tasks, similarly to other types of community-based initiatives. Volunteer computing is based on two pillars: the first is computational - allocating and managing large computing tasks; the second is participative - making large numbers of individuals volunteer their computer resources to a project. While the computational aspects of volunteer computing received much research attention, the participative aspect remains largely unexplored. In this study we aim to address this gap: by drawing on social psychology and online communities research, we develop and test a three-dimensional model of the factors determining volunteer computing users' contribution. We investigate one of the largest volunteer computing projects - [email protected] - by linking survey data about contributors' motivations to their activity logs. Our findings highlight the differences between volunteer computing and other forms of community-based projects, and reveal the intricate relationship between individual motivations, social affiliation, tenure in the project, and resource contribution. Implications for research and practice are discussed.

Abstract In recent years we have witnessed a significant growth of social‐computing communities—online services in which users share information in various forms. As content contributions from participants are critical to the viability of these communities, it is important to understand what drives users to participate and share information with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with various forms of participation in a large online photo‐sharing community. Using survey and system data, we examine four different forms of participation and consider the differences between these forms. We build on theories of motivation to examine the relationship between users' participation and their motivations with respect to their tenure in the community. Amongst our findings, we identify individual motivations (both extrinsic and intrinsic) that underpin user participation, and their effects on different forms of information sharing; we show that tenure in the community does affect participation, but that this effect depends on the type of participation activity. Finally, we demonstrate that tenure in the community has a weak moderating effect on a number of motivations with regard to their effect on participation. Directions for future research, as well as implications for theory and practice, are discussed.

Social computing systems are increasingly a part of people's social environment. Inherent to such communities is the collection and sharing of personal information, which in turn may raise concerns about privacy. In this study, we extend prior research on internet privacy to address questions about antecedents of privacy concerns in social computing communities, as well as the impact of privacy concerns in such communities. The results indicate that users' trust in other community members, and the community's information sharing norms have a negative impact on community-specific privacy concerns. We also find that community-specific privacy concerns not only lead users to adopt more restrictive information sharing settings, but also reduce the amount of information they share with the community. In addition, we find that information sharing is impacted by network centrality and the tenure of the user in the community. Implications of the study for research and practice are discussed.

Abstract In this paper we extend earlier work on the role of the personality trait of resistance to change (RTC) in the adoption of digital libraries. We present an integrative study, drawing on a number of research streams, including IT adoption, social psychology, and digital‐library acceptance. Using structural equation modeling, we confirm RTC as a direct antecedent of effort expectancy. In addition, we also find that by affecting computer anxiety and result demonstrability, RTC acts as an indirect antecedent to both effort expectancy and performance expectancy, which in turn determine user intention to adopt digital library technology. Implications for research and practice are discussed.

We examine tagging behavior on Flickr, a public photo-sharing website. We build on previous qualitative research that exposed a taxonomy of tagging motivations, as well as on social presence research. The motivation taxonomy suggests that motivations for tagging are tied to the intended target audience of the tags --- the users themselves, family and friends, or the general public. Using multiple data sources, including a survey and independent system data, we examine which motivations are associated with tagging level, and estimate the magnitude of their contribution. We find that the levels of the Self and Public motivations, together with social presence indicators, are positively correlated with tagging level; Family & Friends motivations are not significantly correlated with tagging. The findings and the use of survey method carry implications for designers of tagging and other social systems on the web.

Abstract The use of digital libraries has seen steady growth in the past two decades. However, as with other new technologies, effective use of digital libraries depends on user acceptance, which in turn is affected by users' perception of the system's ease of use. Since the introduction of new technologies often involves some form of change for users, the recent identification of the resistance to change (RTC) personality trait, and the development of a scale to measure it, provides an opportunity to assess the impact of RTC on new users of a digital library system. Drawing on prior research focused on personal differences and system characteristics as determinants of perceived ease of use, in the present study we explore the relationship between RTC and perceived ease of use of a university digital library. The results of a survey of 170 new users of the library system suggest that RTC is a significant determinant of perceived ease of use, and improves the explanatory power of previous technology‐acceptance models. Implications of the findings are discussed.