Preliminary Conference Agenda

While many occupations turned to remote work during the COVID-19 pandemic, domestic work by definition requires workers to enter other people’s households, and they often work in close proximity to their employers. With domestic workers proactively handling COVID- 19 risks as part of their already precarious jobs, there is a need for a conceptual understanding of risk management to aid this occupational group during a public health crisis. Our findings emerge from a preliminary qualitative study interviewing occupational groups who adopted risk work practices during the pandemic, providing insight into their risk perceptions and practices. In this paper, we focus on paid domestic workers recruited to investigate how they engaged in situated ‘risk calculations’ to assess different risks present at work. This paper invites an initial discussion on risk practices, communication, and policy to support domestic workers during crises.

The construction of a comprehensive portrait of data searchers represents a crucial yet underexplored topic within the field of data retrieval. To address this gap, this study focuses primarily on two key aspects. Firstly, based on the literature review, this study constructs a comprehensive user attribute classification framework that encompasses 52 user attributes derived from the dimensions of user background, expectancy, experience, and system evaluation and usage intention. Secondly, the present study proposes a deep learning-based multimodal data model for inferring these four-dimensional user attributes. The model posits that leveraging deep learning methods to analyze the features of online behaviors, eye movement, facial expressions, and verbal commentary extracted from multimodal data ena-bles accurate inference of the four-dimensional user attributes. A user ex-periment was conducted to collect data. The results from deep learning and ablation experiments provide strong support for the proposed model. The findings suggest that deep learning analysis of multimodal data facilitates the inference of the four-dimensional user attributes. Notably, the proposed model achieved high accuracy in inferring user expectancy and background. Search behavior features and eye movement features are pivotal in accurate-ly inferring the four-dimensional user attributes.

This study compared the use of social media by British and Chinese news media at the early stage of COVID-19. By analyzing the British Broadcasting Corporation (BBC) Twitter account and China Central Television (CCTV) Weibo account, this research explored the similarities and differences in the health communication strategies applied by two national news media in the UK and China. The content analysis results showed that news updates and official responses were the two most common topics in both BBC Twitter and CCTV Weibo at the beginning of the pandemic. However, CCTV focused more on reporting the statistics of the pandemic, while BBC emphasized the new developments of the pandemic. Besides, the BBC mainly used linked articles to present information, while CCTV used more diverse formats. This study contributes to the global health communication scholarship by considering cultural and organizational factors in the differences of health information dissemination.

Douyin and TikTok belong to the same company, ByteDance, and share similar platform infrastructures. However, they target different markets and operate in distinct platform ecosystems. Unlike copycat versions of Western platforms in mainland China (e.g., the Chinese Twitter—Weibo), Douyin and TikTok originate from the same source but flow into distinct user bases. Following the app walkthrough method and utilizing parallel platformization theory, this study explores the similarities and differences in presenting health information on the two platforms. This research contributes to validating and extending parallel platformization theory in the health domain.

In recent years, the “Young Adult Service,” which underpins the concept of “HOMAGO” proposed by Ito et al. (2009), has gained traction in public libraries, especially in Europe and the United States. In Japan, services that offer experiences with digital media are beginning to be introduced, drawing inspiration from Europe and the United States. Hence, this study seeks to clarify the usage patterns of teenagers at the Yokohama City Central Public Library. In this research, we conducted an observational survey focusing on the usage by teenagers on both the ground and basement floors of the Yokohama City Central Public Library, situated in one of the largest cities in Japan and which has recently undergone renovation. The study took place from the 22nd to the 24th of August and operated daily from 9:30 to 17:00. We examined variables such as the number of user groups, seating occupancy, resources utilised, and the nature of activities undertaken. Our findings indicate that Japanese teenagers predominantly use libraries for personal study but also engage in social chats using smartphones with their peers. These observations suggest that while Japanese teenagers primarily utilise the library for individual study, in group settings they engage in intimate in-teractions, often accompanied by reading or smartphone usage. This highlights a distinct “HOMAGO” (Hanging Out, Messing Around, Geeking Out) behaviour within libraries, emblematic of Japanese youth.

Archive management requires meticulous handling and precise stewardship of textual materials. Large Language Models (large language models (LLMs)), trained extensively on text data, possess exceptional text processing and interpretative capabilities. These allow for profound insights and extractions from the vast troves of information within archives. Anchored in the records life-cycle theory and archivists’ practical operations, this research explores the potential advantages of using LLMs in archival work. We begin by constructing a theoretical framework that demonstrates how LLMs can streamline tasks for archivists. Next, we introduce a novel LLM designed specifically for archive work, called Archival Generative Pre-trained Transformer (ArcGPT), and present its initial performance across four archival tasks. Recognizing that overarching performance metrics may not encapsulate the genuine user experience, we further propose a methodology for a user experiment designed to gauge the user-centric performance of how LLMs support archivists in their archival workflows.

The confluence of artificial intelligence (AI) and artistic creativity signals a pivotal shift in the arts, ushering in a new epoch where human-AI synergy demands further exploration. This research delves deep into the dynamic in-terplay of human and AI within the artistic domain. Through 20 detailed online interviews, we probe the experiential facets of information as humans and AI collaborate in creative endeavors. Our findings indicate that the role of AI in the arts transcends mere automation; it embodies a rich partnership where both human and machine continually learn, adapt, and innovate. This study also presents a model that outlines the information practices central to human-AI collaboration in creative activities. These insights highlight the transformative capabilities of AI, emphasizing the imperative for a more profound comprehension of this partnership and collab-oration. Such understanding is of referential significance, setting the stage for future research and advancements in the sphere of human-AI collaboration creative activities.