Decoding the Symmetry of Influence: A Machine Learning Study of Reading Exposure and Social Attitudes Across Social Groups

The relationship between reading exposure and social attitudes across demographic groups remains a pivotal yet underexplored topic in computational social science. This study adopts a machine learning framework to examine the symmetry of reading’s influence on social attitude formation. Models including Random Forest, XGBoost, LightGBM, and linear regression were employed on data from the 2021 Chinese General Social Survey (CGSS). The results show that reading volume is a key predictor of social attitudes. Moreover, a SHAP-based subgroup analysis revealed that the impact of reading exposure remained stable across gender groups, indicating a symmetric pattern of cognitive influence. This study proposes a methodological pipeline for assessing the symmetry of feature importance in social data, offering actionable insights for researchers and policymakers into the equitable role of media consumption in shaping social cognition.