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During recent crises like COVID-19, microblogging platforms have become popular channels for affected people seeking assistance such as medical supplies and rescue operations from emergency responders and the public. Despite this common practice, the affordances of microblogging services for help-seeking during crises that needs immediate attention are not well understood. To fill this gap, we analyzed 8K posts from COVID-19 patients or caregivers requesting urgent medical assistance on Weibo, the largest microblogging site in China. Our mixed-methods analyses suggest that existing microblogging functions need to be improved in multiple aspects to sufficiently facilitate help-seeking in emergencies, including capabilities of search and tracking requests, ease of use, and privacy protection. We also find that people tend to stick to certain well-established functions for publishing requests, even after better alternatives emerge. These findings have implications for designing microblogging tools to better support help requesting and responding during crises.
Previous qualitative work has documented that platform workers place an immense importance on their reputation due to the use of algorithmic management by online labor platforms. We provide a general experimental method, which can be used across platforms and time, for numerically quantifying the intensity with which platform workers experience reputation system-based algorithmic management. Our method works via an experiment where workers choose between a monetary bonus or a positive review. We demonstrate this method by measuring the value that freelancers assigned to positive feedback on Upwork in June 2020. The median freelancer in our sample valued a single positive review at $\sim$\$49 USD. We also find that less experienced freelancers valued a positive review more highly than those with more experience. Qualitative data collected during the experiment indicates that many freelancers considered issues related to reputation system-based algorithmic management while choosing between the monetary reward and the positive review.
Digital lutherie is a sub-domain of digital craft focused on creating digital musical instruments: high-performance devices for musical expression. It represents a nuanced and challenging area of human-computer interaction that is well established and mature, offering the opportunity to observe designers' work on highly demanding human-computer interfaces.
This paper explores how and why digital luthiers choose their tools and how these tools relate to the challenges they face. Findings from 27 standardised open-ended interviews with prominent digital luthiers from commercial, research, independent and artistic backgrounds are analysed through reflexive thematic analysis. Our discussion explores their perspectives, finding that a process of pragmatic rationalisation and environmental influences play a significant role in tool selection. We also present how challenges faced by digital luthiers relate to social creativity and meta-design. These findings build upon the existing literature that examines the designer-tool relationship.
HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and systems, published over the last two decades. From this review, we identified seven themes associated with the evolution of a project: from establishing community partnerships to sustaining results. Our findings suggest that several tensions characterize these projects, many of which relate to the power and position of researchers, and the computing research environment, relative to community partners. We discuss the implications of our findings and offer methodological proposals to guide HCI, and computing research more broadly, towards practices that center communities.
It is challenging for customers to select appearance building products (e.g., skincare products, weight loss programs) that suit them personally as such products usually demonstrate efficacy only after long-term usage. Although e-retailers generally provide product descriptions or other customers' reviews, users often find it hard to relate to their own situations. In this work, we proposed a pipeline to display envisioned users' appearance after long-term use of appearance building products to deliver their efficacy on each individual visually. We selected skincare as a case and developed SkincareMirror which predicts skincare effects on users' facial images by analyzing product function labels, efficacy ratings, and skin models' images. The results of a between-subjects study (N=48) show that (1) SkincareMirror outperforms the baseline shopping site in terms of perceived usability, usefulness, user satisfaction and helps users select products faster; (2) SkincareMirror is especially effective to males and users with limited product domain knowledge.