A Study on Prediction of New Followers of Social Media Bloggers Based on Principal Component Clustering and Temporal Difference Regression

Abstract

In recent years, social media platforms have profoundly affected people's social interactions and information acquisition, precisely analysing users' needs to achieve efficient matching of content supply, promoting positive ecological cycles, and ultimately enhancing the competitiveness of platforms and commercial value. This paper focuses on the prediction task of "the number of new followers of each blogger", integrates dynamic Bayesian network and collaborative filtering concepts, constructs the "blogger-user interaction matrix", extracts 9-dimensional sliding window differential features of viewing, liking, commenting, etc. in the past three days, and The high-dimensional user features were compressed to 42 dimensions using principal component analysis. Subsequently, K-means was used to divide the 42 bloggers into three groups, and the time-difference OLS regression models were built separately, and the adjusted R² of the three groups was higher than 0.78, which verified the hypothesis that "similar bloggers have similar attention growth patterns". After the model was tested for robustness and sensitivity, the blogger with the most new followers on 21 July was predicted to be B21 (554 people). The research results can provide a quantitative basis for the platform's accurate push and blogger's operation strategy. This paper is better in data dynamic modelling, reviewing a large amount of literature to establish the best model, with more thorough consideration in the problem. The model passes the robustness test and sensitivity test, and has reference value in platform push.