Modeling the Relationship Between Political Ideology and Voter Behavior Using Big Data and Machine Learning Techniques

Abstract

The relationship between political ideology and voter behavior has acquired increasing importance against the backdrop of the emergence of political polarization and rapid changes in the ecosystem of digital information. In this paper, the importance of political ideology for predicting voter behavior will be investigated in the light of the application of machine learning algorithms to multi-source big data. For this purpose, information from multiple sources such as election data, social media communication data, and online political activity data will be analyzed through natural language processing and feature engineering algorithms to identify ideological and behavioral features. Multiple machine learning models including Logistic Regression, Random Forest, and Support Vector Machines are applied to investigate voter behavior and predict voter behavior on the basis of ideological affinity and other features. The findings indicate that political ideology is the best predictor of voter behavior with the average feature importance of 0.42 across different models. The Random Forest model demonstrated the highest predictive accuracy of 87.3% with an AUC-ROC of 0.91, which shows good classification performance. Results also indicate that strongly ideologically oriented voters are more consistent in their voting (86-88%), and are low electorally volatile. Other contributing factors are the sentiment of social media (0.21 importance score) and the level of political engagement (0.15 importance score). The paper finds that political ideology plays a major role in structuring electoral behavior decision-making by voters and that machine learning algorithms can be effectively used to capture complex nonlinear relationships in electoral decision-making by voters. These results indicate the growing role of data-driven methods in political analysis and the development of campaign strategies. Future studies can be done on the topics of longitudinal modeling, cross-national comparisons, and hybrid frameworks that combine the variables of psychology with socio-cultural variables to provide a better predictive and explanatory model.