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Short-form video social media shifts away from the traditional media paradigmby telling the audience a dynamic story to attract their attention. Inparticular, different combinations of everyday objects can be employed torepresent a unique scene that is both interesting and understandable. Offeredby the same company, TikTok and Douyin are popular examples of such new mediathat has become popular in recent years, while being tailored for differentmarkets (e.g. the United States and China). The hypothesis that they expresscultural differences together with media fashion and social idiosyncrasy is theprimary target of our research. To that end, we first employ the FasterRegional Convolutional Neural Network (Faster R-CNN) pre-trained with theMicrosoft Common Objects in COntext (MS-COCO) dataset to perform objectdetection. Based on a suite of objects detected from videos, we performstatistical analysis including label statistics, label similarity, andlabel-person distribution. We further use the Two-Stream Inflated 3D ConvNet(I3D) pre-trained with the Kinetics dataset to categorize and analyze humanactions. By comparing the distributional results of TikTok and Douyin, weuncover a wealth of similarity and contrast between the two closely relatedvideo social media platforms along the content dimensions of object quantity,object categories, and human action categories.

Content-based Analysis of the Cultural Differences between TikTok and Douyin