  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 perform
statistical analysis including label statistics, label similarity, and
label-person distribution. We further use the Two-Stream Inflated 3D ConvNet
(I3D) pre-trained with the Kinetics dataset to categorize and analyze human
actions. By comparing the distributional results of TikTok and Douyin, we
uncover a wealth of similarity and contrast between the two closely related
video social media platforms along the content dimensions of object quantity,
object categories, and human action categories.
