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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.17807 |
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| _version_ | 1866911817099378688 |
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| author | Chen, Si Cheng, Haocong Situ, Jason Kirst, Desirée Su, Suzy Malhotra, Saumya Angrave, Lawrence Wang, Qi Huang, Yun |
| author_facet | Chen, Si Cheng, Haocong Situ, Jason Kirst, Desirée Su, Suzy Malhotra, Saumya Angrave, Lawrence Wang, Qi Huang, Yun |
| contents | Previous research underscored the potential of danmaku--a text-based commenting feature on videos--in engaging hearing audiences. Yet, for many Deaf and hard-of-hearing (DHH) individuals, American Sign Language (ASL) takes precedence over English. To improve inclusivity, we introduce "Signmaku," a new commenting mechanism that uses ASL, serving as a sign language counterpart to danmaku. Through a need-finding study (N=12) and a within-subject experiment (N=20), we evaluated three design styles: real human faces, cartoon-like figures, and robotic representations. The results showed that cartoon-like signmaku not only entertained but also encouraged participants to create and share ASL comments, with fewer privacy concerns compared to the other designs. Conversely, the robotic representations faced challenges in accurately depicting hand movements and facial expressions, resulting in higher cognitive demands on users. Signmaku featuring real human faces elicited the lowest cognitive load and was the most comprehensible among all three types. Our findings offered novel design implications for leveraging generative AI to create signmaku comments, enriching co-learning experiences for DHH individuals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17807 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-Hearing Chen, Si Cheng, Haocong Situ, Jason Kirst, Desirée Su, Suzy Malhotra, Saumya Angrave, Lawrence Wang, Qi Huang, Yun Human-Computer Interaction F.2.2; I.2.7 Previous research underscored the potential of danmaku--a text-based commenting feature on videos--in engaging hearing audiences. Yet, for many Deaf and hard-of-hearing (DHH) individuals, American Sign Language (ASL) takes precedence over English. To improve inclusivity, we introduce "Signmaku," a new commenting mechanism that uses ASL, serving as a sign language counterpart to danmaku. Through a need-finding study (N=12) and a within-subject experiment (N=20), we evaluated three design styles: real human faces, cartoon-like figures, and robotic representations. The results showed that cartoon-like signmaku not only entertained but also encouraged participants to create and share ASL comments, with fewer privacy concerns compared to the other designs. Conversely, the robotic representations faced challenges in accurately depicting hand movements and facial expressions, resulting in higher cognitive demands on users. Signmaku featuring real human faces elicited the lowest cognitive load and was the most comprehensible among all three types. Our findings offered novel design implications for leveraging generative AI to create signmaku comments, enriching co-learning experiences for DHH individuals. |
| title | Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-Hearing |
| topic | Human-Computer Interaction F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2403.17807 |