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

作　　者：Liangkeyi SUN 

机构地区：[1]University of Macao

出　　处：《计算社会科学》2025年第1期37-53,共17页Computational Social Science

摘　　要：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.

关 键 词：Social Media Data Causal Inference User Behavior Patterns Propensity Score Matching DISCONTINUITY Data Preprocessing 

分 类 号：TN9[电子电信—信息与通信工程]