Value Assessment of UGC Short Videos through Element Mining and Data Analysis

Abstract

UGC short videos play a crucial role in sharing information and disseminating content in the era of new information technology. Accurately assessing the value of UGC short videos is highly significant for the sustainable development of self-media platforms and the secure governance of cyberspace. This study proposes a method for assessing the value of UGC short videos from the perspective of element mining and data analysis. The method involves three steps. Firstly, the text clustering algorithm and topic mapping visualization technology are utilized to identify elements for assessing the value of UGC short videos and construct an assessment index system. Secondly, structured data indexes are quantified using platform data statistics, while unstructured data indexes are quantified using the LSTM fine-grained sentiment analysis model. Lastly, the VIKOR model, incorporating an improved gray correlation coefficient, is employed to effectively evaluate the value of UGC short videos. The empirical results indicate that the value of current domestic UGC short videos is primarily associated with three dimensions: the creators, the platforms, and the users. It encompasses 11 value elements, including fan popularity, economic returns of creation, and frequency of interaction. Additionally, we assess the value of short videos within the mainstream partitions of the Bilibili platform and generate a value radar chart. Our findings reveal that short videos in game partitions generate higher revenue for creators and platforms but may neglect users’ needs for knowledge, culture, and other content. Conversely, short videos in the knowledge, food, and music partitions demonstrate specific distinctions in fulfilling users’ requirements. Ultimately, we offer personalized recommendations for the future development of high-value UGC short videos within the mainstream partitions.