Recent research within the HCI community has illuminated the challenges faced by marginalized groups on algorithm-driven livestreaming platforms. However, there is a notable gap in understanding how elderly livestreamers interact with the platform content moderation and algorithmic (in)visibility. This study investigates the perceptions of the algorithm-moderated (in)visibility and the coping strategies of 16 elderly streamers on Douyin. We find that, contrary to stereotypes of elderly users as digitally uninformed, these streamers actively engage with the platform to facilitate their understanding about platform algorithm. This engagement involves official guidance, peer learning, and personal experimentation. The streamers adopt various strategies to align with the perceived algorithmic preferences. Despite their rich knowledge about the platform's visibility moderation, many elderly streamers face significant challenges, such as physical and psychological strain and low viewer traffic. We conclude with design implications for livestreaming platforms to foster fairness and promote engagement among elderly streamers.

This study investigates the use of generative AI with the Rorschach Inkblot Test to support emotional exploration and self-expression. A controlled experiment compared static images with AI-generated dynamic visuals, and expert interviews provided professional insights. Results indicate that dynamic visuals can deepen emotional exploration, enrich narratives, and expand self-expression, though individual differences and content variability remain challenges. This work offers a novel methodology for integrating generative AI into psychological assessment and creative arts while highlighting its potential and ethical considerations.

Livestream shopping platforms often overlook the accessibility needs of the Deaf and Hard of Hearing (DHH) community, leading to barriers such as information inaccessibility and overload. To tackle these challenges, we developed EchoAid, a mobile app designed to improve the livestream shopping experience for DHH users. EchoAid utilizes advanced speech-to-text conversion, Rapid Serial Visual Presentation (RSVP) technology, and Large Language Models (LLMs) to simplify the complex information flow in live sales environments. We conducted exploratory studies with eight DHH individuals to identify design needs and iteratively developed the EchoAid prototype based on feedback from three participants. We then evaluate the performance of this system in a user study workshop involving 38 DHH participants. Our findings demonstrate the successful design and validation process of EchoAid, highlighting its potential to enhance product information extraction, leading to reduced cognitive overload and more engaging and customized shopping experiences for DHH users.