Paper Reading AI Learner

'It took me almost 30 minutes to practice this'. Performance and Production Practices in Dance Challenge Videos on TikTok

2020-08-29 19:23:25
Daniel Klug

Abstract

TikTok is a music-based video sharing social media app famous for users creating short meme and dance videos. TikTok videos are largely based on popular song snippets, which is why lip syncing and dance moves evolve as significant user performance practices in videos. User prosumption has not yet been studied regarding the characteristics of TikTok. This paper is based on social practice and performance theory, social media studies, and participatory online video culture. It uses the #distantdance challenge on TikTok to analyze production practices and strategies of users through qualitative video product analysis. 92 videos were coded and categorized regarding their visual content (who participated in which way) and paratextual elements (used tags and captions). The visual and (para-)textual elements were then analyzed regarding indicators that allow to draw conclusions on users' video creation strategies and performance practices in participating in the #distantdance challenge. The results show videos are mainly performed by single white female teenagers wearing casual outfits in their bedrooms. Users shared their experiences about learning and performing the dance in video captions. While users prepared settings and outfits for their performance, the majority of performances seems rather unplanned or spontaneous. This indicates most videos might be part of a series of user attempts to master the dance challenge resulting in posting the first successful video performance to TikTok. In addition to the dance moves, participants also added gestures as closing elements to their performances. This indicates their knowledge of using signals as part of an online community while at the same time manifesting their belongingness to the community. These first results of a qualitative product analysis illustrate some of users' motivations and effort to participate in TikTok dance challenges.

Abstract (translated)

URL

https://arxiv.org/abs/2008.13040

PDF

https://arxiv.org/pdf/2008.13040.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot