Water pollution remains a critical global challenge, threatening public health and aquatic ecosystems. Governmental efforts to monitor and manage water resources often face limitations due to constrained resources and socio-political priorities that may not prioritize sustainable solutions. Citizen science has emerged as a promising approach, engaging communities in scientific research to expand data collection capabilities and foster environmental stewardship. This paper explores the role of distributed collaboration infrastructure, exemplified by the Water Data Collaborative (WDC), in connecting and enhancing citizen-based water quality monitoring groups across North America. Through participatory design sessions with WDC users, the study identifies essential design features needed to support collaboration and address common challenges such as data standardization and resource sharing. Our findings emphasize the complexity of relationships among citizen science groups, government entities, and higher-order organizations, emphasizing the need for scalable, integrated solutions that avoid creating new silos within the ecosystem. This research contributes insights into how CSCW and HCI can facilitate effective citizen science practices and infrastructure design to advance sustainable water management.

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

Starting from the spring of 2020, higher institutions in the US underwent a rapid shift from in-person classes to emergency remote education, in response to the COVID-19 outbreak. Under this circumstance, a variety of video conferencing tools (e.g., Zoom) have been adopted for distance education, which pose a set of new challenges arising from synchronous online classes. Among these, one significant issue was students' unwillingness to open cameras, resulting in a lack of non-verbal cues that instructors could rely on to gauge students' understanding and adjust their teachings. Towards addressing this issue, our qualitative study aims at investigating the rationales behind students' camera avoidance. Through a series of semi-structured interviews on undergraduate students in the U.S, we identified prominent factors -- namely the class size, lecture style, level of interactivity and privacy concerns -- that influenced students' motivation for opening their cameras. At the same time, we uncovered several difficulties, such as heightened self-awareness, feeling of minority and academic perspective, that discouraged students from opening camera, with more substantial impacts on international students. We conclude with actionable insights into the design of online classes, video-conferencing platforms and camera technology that can promote camera usage, thereby contributing to scalable and inclusive interventions for facilitating the transition into remote education.

Volunteers in non-profit groups are a valuable workforce that contributes to economic development and supports people in need in the U.S. However, many non-profit groups face challenges including engaging and sustaining volunteer participation, as well as increasing visibility of their work in the community. To support non-profit groups' service, we explored how engaging community members in the volunteer-acknowledgment process may have an impact. We set up workstations and invited community members to write thank-you cards to volunteers in non-profit groups. We conducted 14 interviews with volunteers and community members, collected and analyzed 25 thank-you cards. We found that the acknowledgment activity can help circulate social goods through multiple stakeholders, that authenticity was valued in the acknowledgment process, and that non-profit groups intended to distribute, reuse, and publicize the acknowledgments to utilize them to a fuller extent. Our contributions include expanding knowledge on experiences, needs, and impact of community acknowledgment from different stakeholders, as well as presenting design opportunities.

Citizen science project leaders collecting field data in a hyperlocal community often face common socio-technical challenges, which can potentially be addressed by sharing innovations across different groups through peer-to-peer collaboration. However, most citizen science groups practice in isolation, and end up re-inventing the wheel when it comes to addressing these common challenges. This study seeks to investigate distributed collaboration between different water monitoring citizen science groups. We discovered a unique social network application called Water Reporter that mediated distributed collaboration by creating more visibility and transparency between groups using the app. We interviewed 8 citizen science project leaders who were users of this app, and 6 other citizen science project leaders to understand how distributed collaboration mediated by this app differed from collaborative practices of Non Water Reporter users. We found that distributed collaboration was an important goal for both user groups, however, the tasks that support these collaboration activities differed for the two user groups.

The COVID-19 global pandemic brought forth wide-ranging, unanticipated changes in human interaction, as communities rushed to slow the spread of the coronavirus. In response, local geographic community members created grassroots care-mongering groups on social media to facilitate acts of kindness, otherwise known as care-mongering. In this paper, we are interested in understanding the types of care-mongering that take place and how such care-mongering might contribute to community collective efficacy (CCE) and community resilience during a long-haul global pandemic. We conducted a content analysis of a care-mongering group on Facebook to understand how local community members innovated and developed care-mongering practices online. We observed three facets of care-mongering: showing appreciation for helpers, coming up with ways of supporting one another's needs, and continuing social interactions online and present design recommendations for further augmenting care-mongering practices for local disaster relief in online groups.

Remote sighted assistance (RSA) is an emerging navigational aid for people with visual impairments (PVI). Using scenario-based design to illustrate our ideas, we developed a prototype showcasing potential applications for computer vision to support RSA interactions. We reviewed the prototype demonstrating real-world navigation scenarios with an RSA expert, and then iteratively refined the prototype based on feedback. We reviewed the refined prototype with 12 RSA professionals to evaluate the desirability and feasibility of the prototyped computer vision concepts. The RSA expert and professionals were engaged by, and reacted insightfully and constructively to the proposed design ideas. We discuss what we learned about key resources, goals, and challenges of the RSA prosthetic practice through our iterative prototype review, as well as implications for the design of RSA systems and the integration of computer vision technologies into RSA.

Online symptom checkers (OSC) are widely used intelligent systems in health contexts such as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as machine learning to facilitate self-diagnosis and triage based on symptoms input by healthcare consumers. However, intelligent systems’ lack of transparency and comprehensibility could lead to unintended consequences such as misleading users, especially in high-stakes areas such as healthcare. In this paper, we attempt to enhance diagnostic transparency by augmenting OSCs with explanations. We first conducted an interview study (N=25) to specify user needs for explanations from users of existing OSCs. Then, we designed a COVID-19 OSC that was enhanced with three types of explanations. Our lab-controlled user study (N=20) found that explanations can significantly improve user experience in multiple aspects. We discuss how explanations are interwoven into conversation flow and present implications for future OSC designs.

Studies have shown that interpersonal relationships such as families and friends are an important source of support and encouragement to those who seek to engage in healthier habits. However, challenges related to geographic distance may hinder those relationships from fully collaborating and engaging in healthy living together. To explore this domain, we developed and deployed a lightweight photo-based application called PhamilySpace with a week-long intervention. Our goal is to examine family members' and friends' engagement and awareness on healthy behaviors while living apart. Our analysis of the semi-structured interviews, pre/post-intervention instruments, and application logs suggests three main benefits of interventions for health promotion in this context: (1) increased awareness on acts of health; (2) reciprocal sharing of health information supports social accountability over distance; and (3) positive dialogue around health enhances support on healthy living. By providing insights into distributed family/friends interactions and experiences with the application, we identify benefits, challenges, and opportunities for future design interventions that promote healthy behaviors.

Crisis informatics research has examined geographically bounded crises, such as natural or man-made disasters, identifying the critical role of local and hyper-local information focused on one geographic area in crisis communication. The COVID-19 pandemic represents an understudied kind of crisis that simultaneously hits locales across the globe, engendering an emergent form of crisis communication, which we term cross-local communication. Cross-local communication is the exchange of crisis information between geographically dispersed locales to facilitate local crisis response. To unpack this notion, we present a qualitative study of an online migrant community of overseas Taiwanese who supported fellow Taiwanese from afar. We detail four distinctive types of cross-local communication: situational updates, risk communication, medical consultation, and coordination. We discuss how the current pandemic situation brings new understandings to crisis informatics and online health community literature, and what role digital technologies could play in supporting cross-local communication.

People with visual impairments (PVI) must interact with a world they cannot see. Remote sighted assistance (RSA) has emerged as a conversational assistive technology. We interviewed RSA assistants ("agents") who provide assistance to PVI via a conversational prosthetic called Aira (https://aira.io/) to understand their professional practice. We identified four types of support provided: scene description, navigation, task performance, and social engagement. We discovered that RSA provides an opportunity for PVI to appropriate the system as a richer conversational/social support tool. We studied and identified patterns in how agents provide assistance and how they interact with PVI as well as the challenges and strategies associated with each context. We found that conversational interaction is highly context-dependent. We also discuss implications for design.

The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this article, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users’ subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs.

Hybrid social recommender systems use social relevance from multiple sources to recommend relevant items or people to users. To make hybrid recommendations more transparent and controllable, several researchers have explored interactive hybrid recommender interfaces, which allow for a user-driven fusion of recommendation sources. In this field of work, the intelligent user interface has been investigated as an approach to increase transparency and improve the user experience. In this paper, we attempt to further promote the transparency of recommendations by augmenting an interactive hybrid recommender interface with several types of explanations. We evaluate user behavior patterns and subjective feedback by a within-subject study (N=33). Results from the evaluation show the effectiveness of the proposed explanation models. The result of post-treatment survey indicates a significant improvement in the perception of explainability, but such improvement comes with a lower degree of perceived controllability.

Recommender system helps users to reduce information overload. In recent years, enhancing explainability in recommender systems has drawn more and more attention in the field of Human-Computer Interaction (HCI). However, it is not clear whether a user-preferred explanation interface can maintain the same level of performance while the users are exploring or comparing the recommendations. In this paper, we introduced a participatory process of designing explanation interfaces with multiple explanatory goals for three similarity-based recommendation models. We investigate the relations of user perception and performance with two user studies. In the first study (N=15), we conducted card-sorting and semi-interview to identify the user preferred interfaces. In the second study (N=18), we carry out a performance-focused evaluation of six explanation interfaces. The result suggests that the user-preferred interface may not guarantee the same level of performance.

The beyond accuracy user experience of using recommender system is drawing more and more attention. For example, the system interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how the interfaces can constitute the user experience and the social interactions. In this paper, I plan to propose a visual diversity-enhanced interface that supports the user to inspect and control the multi-relevance recommendations. The goal is to let the users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two preliminary user studies with real-life tasks were conducted to compare the visual interface to a standard ranked list interface. The users» subjective evaluations show significant improvement in many metrics. I further show that the users explored a diverse set of recommended items while experiencing an increase in overall user satisfaction. A user-centered evaluation was used to reveal the mediating effects between the subjective and objective conceptual components. The future plans are discussed to extend the current findings.

The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this paper, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users» subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs.

A social recommender system aims to provide useful suggestion to the user and prevent social overload problem. Most of the research efforts are spent on push high relevant item on top of the ranked list, using a weight ensemble approach. However, we argue the ``learned'' static fusion is not enough to specific contexts. In this paper, we develop a series visual recommendation components and control panel for the user to interact with the recommendation result of an academic conference. The system offers a better recommendation transparency and user-driven fusion through recommended sources. The experiment result shows the user did fuse the different recommended sources and exploration patterns among tasks. The post-study survey is positively associated with the system and explanation function effectiveness. This finding shed light on the future research of design a recommender system with human intervention and the interface beyond the static ranked list.

This note reports on the use of ICTs by a small nonprofit organization that serves LGBT youth. Our work centers on a reflective evaluation of the use of online communities for LGBT community through qualitative interviews with the organization. Perceived issues around ICT use in the organization were shaped by the blurred lines between professional and personal interactions online, the small size of the community and ubiquity of social media use, and ambivalence of members toward online communication. The project models one way for researchers in ICT4D to work within communities to develop an understanding of self-identified issues in vulnerable populations.