Research on Privacy Infringement Risks and Governance Pathways of Targeted Advertising on Short-Video Platforms from an Algorithmic Perspective: A Case Study of Tik Tok and Rednote

Abstract

Algorithm recommendation technology enables short video platforms to accurately push advertisements based on user portraits. However, this “thousands of people and thousands of faces” advertising distribution model is based on the continuous collection and analysis of users ' personal information, which also brings a series of privacy disputes. Taking Tik Tok and Rednote as examples, this paper examines the algorithm operation mode and privacy risk behind personalized advertising. The study found that the privacy risk of short video targeted advertising does not appear alone in a certain link, but gradually occurs in the complete process of data collection, processing and delivery. Based on the above situation, this paper discusses the possible governance ideas from three aspects: improving the specific rules of automatic decision-making, improving the openness of the algorithm, and establishing a variety of supervision methods.