"Because Some Sighted People, They Don't Know What the Heck You're Talking About:" A Study of Blind TikTokers' Infrastructuring Work to Build Independence
October 11, 2023 Β· Declared Dead Β· π Proc. ACM Hum. Comput. Interact.
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yao Lyu, John M. Carroll
arXiv ID
2310.07154
Category
cs.HC: Human-Computer Interaction
Citations
12
Venue
Proc. ACM Hum. Comput. Interact.
Last Checked
4 months ago
Abstract
There has been extensive research on the experiences of individuals with visual impairments on text- and image-based social media platforms, such as Facebook and Twitter. However, little is known about the experiences of visually impaired users on short-video platforms like TikTok. To bridge this gap, we conducted an interview study with 30 BlindTokers (the nickname of blind TikTokers). Our study aimed to explore the various activities of BlindTokers on TikTok, including everyday entertainment, professional development, and community engagement. The widespread usage of TikTok among participants demonstrated that they considered TikTok and its associated experiences as the infrastructure for their activities. Additionally, participants reported experiencing breakdowns in this infrastructure due to accessibility issues. They had to carry out infrastructuring work to resolve the breakdowns. Blind users' various practices on TikTok also foregrounded their perceptions of independence. We then discussed blind users' nuanced understanding of the TikTok-mediated independence; we also critically examined BlindTokers' infrastructuring work for such independence.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted