Semantic Scholar Open Access 2024 23 sitasi

Mapping the Design Space of Teachable Social Media Feed Experiences

K. Feng Xander Koo Lawrence Tan Amy Bruckman David W. McDonald +1 lainnya

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Abstrak

Social media feeds are deeply personal spaces that reflect individual values and preferences. However, top-down, platform-wide content algorithms can reduce users’ sense of agency and fail to account for nuanced experiences and values. Drawing on the paradigm of interactive machine teaching (IMT), an interaction framework for non-expert algorithmic adaptation, we map out a design space for teachable social media feed experiences to empower agential, personalized feed curation. To do so, we conducted a think-aloud study (N = 24) featuring four social media platforms—Instagram, Mastodon, TikTok, and Twitter—to understand key signals users leveraged to determine the value of a post in their feed. We synthesized users’ signals into taxonomies that, when combined with user interviews, inform five design principles that extend IMT into the social media setting. We finally embodied our principles into three feed designs that we present as sensitizing concepts for teachable feed experiences moving forward.

Topik & Kata Kunci

Computer Science

Penulis (6)

K. Feng

Xander Koo

Lawrence Tan

Amy Bruckman

David W. McDonald

Amy X. Zhang

Format Sitasi

APA MLA BibTeX

Feng, K., Koo, X., Tan, L., Bruckman, A., McDonald, D.W., Zhang, A.X. (2024). Mapping the Design Space of Teachable Social Media Feed Experiences. https://doi.org/10.1145/3613904.3642120

Akses Cepat

Lihat di Sumber doi.org/10.1145/3613904.3642120

Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Total Sitasi: 23×
Sumber Database: Semantic Scholar
DOI: 10.1145/3613904.3642120
Akses: Open Access ✓