Content creators face heightened risks of online harm due to their public visibility and the increasing hostility in digital spaces. Traditional moderation tools, such as reporting and deletion, often fall short in addressing the emotional and reputational impact of online hate. In this study, we explore how community-driven approaches like counterspeech may support creators in navigating hateful comments, and how AI-powered tools could assist in this process. We conducted qualitative interviews and concept testing with 15 active content creators from Chinese social media platforms to understand their experiences and expectations. Our findings reveal that creators' approaches to counterspeech are shaped by their professional roles, the need to maintain authenticity, and the pressures of audience management. While creators saw value in AI-assisted counterspeech for improving efficiency and tone, they expressed concerns about misrepresentation, loss of control, and unintended harm. They preferred AI tools that augmented their voice rather than replaced it, and emphasized the importance of balancing emotional nuance with scalability. These insights inform the design of creator-centered AI moderation tools that integrate human agency and context sensitivity, contributing to healthier and more sustainable online environments.

Collaborative care management is an evidence-based approach to integrated psychosocial care for patients with comorbid cancer and depression. Prior work highlights challenges in patient-provider collaboration in navigating parallel cancer care and psychosocial care journeys of these patients. We design and deploy SCOPE , a platform for technology-enhanced collaborative care combining a patient-facing mobile app with a provider-facing registry. We examine SCOPE through a total of 45 interviews with patients and providers conducted in SCOPE 's 15 months of design and development and 24 Months of SCOPE 's deployment for actual care in 6 cancer clinics. We find that: (1) SCOPE supported patient engagement in its underlying collaborative care and behavioral activation interventions, (2) patient-generated data in SCOPE improved patient-provider collaboration between and within in-person sessions, (3) SCOPE supported providers in delivering care and improved care team collaboration, (4) experience with SCOPE created evolving expectations for collaboration around data, and (5) SCOPE 's deployment in actual care surfaced important implementation barriers. We discuss the implications of our findings in terms of designing for engagement with behavioral health interventions, negotiating patient data sharing and provider responsiveness, supporting personalized self-tracking goals in evidence-based interventions, exploring the role of digital health navigators in technology-enhanced care, and the need for flexibility in aligning technology-supported interventions to patient needs.

The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable greater study of this question, we present LegalBench: a collaboratively constructed legal reasoning benchmark consisting of 162 tasks covering six different types of legal reasoning. LegalBench was built through an interdisciplinary process, in which we collected tasks designed and hand-crafted by legal professionals. Because these subject matter experts took a leading role in construction, tasks either measure legal reasoning capabilities that are practically useful, or measure reasoning skills that lawyers find interesting. To enable cross-disciplinary conversations about LLMs in the law, we additionally show how popular legal frameworks for describing legal reasoning—which distinguish between its many forms—correspond to LegalBench tasks, thus giving lawyers and LLM developers a common vocabulary. This paper describes LegalBench, presents an empirical evaluation of 20 open-source and commercial LLMs, and illustrates the types of research explorations LegalBench enables.

ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) is considered one of the premium forums for research on accessibility. Recently, Mack et al. shed light on the demographics, goals, research methodologies, and evolution of accessibility research over time. We extend their work by exploring the frequencies and trends of disability categories and computer science research domains in publications at ASSETS (N=1,678). Our results show that disability categories and research domains varied significantly across the publication years. We found that in the past 10 years, publications targeting Mental-Health-Related disabilities and the research domain of AR/VR show an increasing trend. In opposition, Gaming, Input Methods/Interaction Techniques, and User Interfaces domains portray a decreasing trend. Additionally, our results show that the majority of the publications utilize the AI/ML/CV/NLP domain (19%) and focus on people with visual disabilities (42%). We share our preliminary exploration results and identify avenues for future work.

Social media, blogs, podcasts, and other computer-mediated communication technology have become an integral way for the public to access and engage with research. However, despite the evolving challenges researchers face navigating these platforms, and the high stakes of online science communication, relatively little HCI research has focused on understanding and supporting online science communication through these participatory platforms. Through a review of the literature and a set of interviews with HCI researchers (n = 24), we identify challenges currently facing researchers who try to engage with the public about their work, and establish a research agenda for HCI to study, design, and evaluate technology to support science communication. Specifically, we advocate for the design of tools to support audience analytics, automated summary and outreach workflows, and providing quantitative and qualitative feedback about online outreach efforts, as well as additional research to elucidate the impacts of self-directed science communication efforts and the evolving roles of scientists on the participatory web. With shifting online platforms placing researchers in the role of advocates and participants in science communication, understanding and supporting these interactions is now more important than ever.

Live streaming is a rapidly growing industry, with millions of content creators using platforms like Twitch to share games, art, and other activities. However, with this rise in popularity, most streamers often fail to attract viewers and grow their platforms. Analytic tools—which have shown success in other business and learning contexts—may be one potential solution, but their use in streaming settings remains unexplored. In this study, we focused on game streaming and interviewed 18 game streamers on Twitch and Mixer about their information needs and current use of tools, supplemented by explorations into their Discord communities. We find that streamers have a range of content, marketing, and community information needs, many of which are not being met by available tools. We conclude with design implications for developing more streamer-centered analytics for video game streamers.

Online sources such as social media have become increasingly important for the proliferation of science news, and past research has shown that reading user-generated comments after an article (i.e. in typical online news and blog formats) can impact the way people perceive the article. However, recent studies have shown that people are likely to read the comments before the article itself, due to the affordances of platforms like Reddit which display them up-front. This leads to questions about how comments can affect people's expectations about an article, and how those expectations impact their interest in reading it at all. This paper presents two experimental studies to better understand how the presentation of comments on Reddit affects people's engagement with science news, testing potential mediators such as the expected difficulty and quality of the article. Study 1 provides experimental evidence that difficult comments can reduce people's interest in reading an associated article. Study 2 is a pre-registered follow-up that uncovers a similarity heuristic; the various qualities of a comment (difficulty, information quality, entertainment value) signal that the article will be of similar quality, ultimately affecting participants' interest in reading it. We conclude by discussing design implications for online science news communities.

Depression is common but under-treated in patients with cancer, despite being a major modifiable contributor to morbidity and early mortality. Integrating psychosocial care into cancer services through the team-based Collaborative Care Management (CoCM) model has been proven to be effective in improving patient outcomes in cancer centers. However, there is currently a gap in understanding the challenges that patients and their care team encounter in managing co-morbid cancer and depression in integrated psycho-oncology care settings. Our formative study examines the challenges and needs of CoCM in cancer settings with perspectives from patients, care managers, oncologists, psychiatrists, and administrators, with a focus on technology opportunities to support CoCM. We find that: (1) patients with co-morbid cancer and depression struggle to navigate between their cancer and psychosocial care journeys, and (2) conceptualizing co-morbidities as separate and independent care journeys is insufficient for characterizing this complex care context. We then propose the parallel journeys framework as a conceptual design framework for characterizing challenges that patients and their care team encounter when cancer and psychosocial care journeys interact. We use the challenges discovered through the lens of this framework to highlight and prioritize technology design opportunities for supporting whole-person care for patients with co-morbid cancer and depression.