Social Media Data Analysis: A Causal Inference Based Study of User Behavior Patterns

Abstract

This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns. By leveraging large-scale social media datasets, this research develops a systematic analytical framework that integrates techniques such as propensity score matching, regression analysis, and regression discontinuity design to identify the causal effects of content characteristics, user attributes, and social network structures on user interactions, including clicks, shares, comments, and likes. The empirical findings indicate that factors such as sentiment, topical relevance, and network centrality have significant causal impacts on user behavior, with notable differences observed among various user groups. This study not only enriches the theoretical understanding of social media data analysis but also provides data-driven decision support and practical guidance for fields such as digital marketing, public opinion management, and digital governance.