Content-based Analysis of the Cultural Differences between TikTok and Douyin
Li Sun, Haoqi Zhang, Songyang Zhang, Jiebo Luo

TL;DR
This study compares TikTok and Douyin to analyze cultural differences through object detection and action recognition in videos, revealing similarities and contrasts in content dimensions across the platforms.
Contribution
It introduces a novel content analysis method combining object detection and action recognition to compare cultural differences between TikTok and Douyin.
Findings
Similarities in object and action distributions across platforms
Distinct differences reflecting cultural preferences
Quantitative analysis of content features
Abstract
Short-form video social media shifts away from the traditional media paradigm by telling the audience a dynamic story to attract their attention. In particular, different combinations of everyday objects can be employed to represent a unique scene that is both interesting and understandable. Offered by the same company, TikTok and Douyin are popular examples of such new media that has become popular in recent years, while being tailored for different markets (e.g. the United States and China). The hypothesis that they express cultural differences together with media fashion and social idiosyncrasy is the primary target of our research. To that end, we first employ the Faster Regional Convolutional Neural Network (Faster R-CNN) pre-trained with the Microsoft Common Objects in COntext (MS-COCO) dataset to perform object detection. Based on a suite of objects detected from videos, we…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVisual Attention and Saliency Detection · Video Surveillance and Tracking Methods · Human Pose and Action Recognition
