This study examines the usability of a virtual space designed to foster digital resilience and support adolescents affected by cyberbullying. The platform, Cyber Re:New Café, aims to provide a safe, supportive, and empowering space where young people can develop coping strategies, build resilience skills, and engage meaningfully with peers and experts. Usability testing was conducted with ten purposively selected participants, including LGBTQI+ adolescents who had experienced cyberbullying, teacher counselors, and a psychologist. Participants interacted with the platform by completing scenario-based tasks. Data were collected through think-aloud protocols and semi-structured interviews guided by Nielsen’s heuristics, which informed the development of key usability components for youth virtual spaces. Findings indicate that the platform was well-received in terms of accessibility, emotional safety, and user autonomy. However, participants recommended enhancements such as adding shortcut menus for smoother navigation, real-time online status indicators to improve system clarity, and interest-based grouping or designated facilitators to foster safer and more engaging peer interactions. The study synthesized usability components tailored to virtual spaces, which encompass six key dimensions: access, control, feedback, social engagement, emotional resonance, and sustained motivation. This research contributes to HCI by demonstrating how platforms can be designed not only for functional usability but also to promote emotional safety, empowerment, and collective digital resilience among vulnerable youth populations.
Hand-drawn anime production generates various intermediate products, such as key drawings, modified key drawings, in-between drawings, and timesheets. These products are paper-based and describe the visual and temporal structure of each cut in a video. However, existing archives generally digitize and store them only as static images, omitting the relationships of timing, layering, and revisions in the animation process. We propose a digital archive system that reconstructs and visualizes these relationships by extracting structural information from timesheets and use it as metadata to link images. We extended the International Image Interoperability Framework (IIIF) manifest to describe frame-by-frame timing, multi-layer composition, and revision histories. Our interactive viewer provides synchronized playback, layer controls, and timeline navigation. In a user experiment involving 20 participants, our system significantly enhanced understanding of motion flow, layer composition, and revision intent compared to a conventional archive system. While participants still found symbolic notations in timesheets difficult to interpret, the proposed system helped them grasp the production process more easily.
Generating high-quality descriptions for web archives remains a time-consuming bottleneck in digital preservation workflows. This paper explores the use of large language models (LLMs) to automate this task, focusing on fine-tuning the Qwen3-8B model on a curated corpus of human-written summaries. The resulting system produces semantically accurate and context-aware descriptions of HTML records extracted from WARC files. We integrate the model into a full-stack pipeline that handles ingestion, parsing, and AI-driven analysis. Performance is evaluated using BERTScore and MoverScore, alongside manual assessments by archivists, librarians, and archival science students. Results show low Composite Edit Rates (CER) and high user satisfaction, validating both the reliability and utility of LLMs for metadata generation. These findings highlight the potential of AI to enhance scalability, consistency, and quality in archival description practices.
As generative AI systems like ChatGPT become integrated into daily routines, understanding how different users engage with these tools over time is essential for designing future information services. This paper presents an empirical user study examining ChatGPT usage patterns across a diverse set of 38 participants. Using exported chat histories, we analyze over 82,000 prompts spanning up to 21 months per participant. Our findings reveal distinct interaction behaviors tied to age, gender, and time of day. Notably, older users exhibit stronger work-week patterns in usage, while women show higher activity at night. These results highlight the roles generative AI plays in users’ lives, extending beyond productivity into moments of reflection, support, and multitasking.
This study examines the implementation and impact of the ACM-THU Open transformative agreement at Tsinghua University. Quantitative content analysis of 1,317 ACM publications from 2021 to 2024 by Tsinghua authors reveals that the ACM-THU Open agreement has effectively increased open access publishing, with all articles in 2022 and 2023 being openly accessible. However, challenges remain in author engagement and copyright licensing, as 35
