Political Participation, Particularly Electoral Participation, as a Key Component of Democratic Processes: An Analysis and Prediction Using Deep Learning and Ensemble Learning Techniques

Political participation, especially participation in elections, is one of the key components of democratic processes, influenced by a range of social, economic, and psychological factors. This study focuses on analyzing and predicting electoral participation rates in the Islamic Republic of Iran using a dataset derived from a public opinion survey. The dataset includes 10,000 records featuring individual and social attributes such as gender, age, education level, economic status, employment, and media interactions.

To conduct predictive analyses, advanced deep learning models (neural networks) and ensemble learning techniques, such as Random Forest and Gradient Boosting, were employed. The empirical findings indicate that education level, monthly income, political beliefs, media engagement, and trust in the political system significantly impact individuals’ decisions to participate in elections. Moreover, the accuracy of machine learning models in predicting participation improved considerably when social and economic features were incorporated.

This research provides a valuable tool for analysts and electoral policymakers, enabling them to develop more effective strategies for increasing voter participation and enhancing the accuracy of electoral predictions. Additionally, the findings of this study offer a foundation for future research in the simulation and prediction of electoral behaviors.