Big Data in Electoral Studies

Electoral behaviour research evolves through digital transformation, using big data for predictive analysis and data-driven methods. This paper explores how political science benefits from evaluating large datasets to detect voter behaviour patterns. Researchers now use voter registration, social media analytics, polls, and economic indicators, supported by data science and geographic visualization tools. The integration of natural language processing and machine learning enhances accuracy in sentiment analysis, voter forecasting, and population comparisons. The chapter also discusses challenges in managing vast electoral data, including privacy risks, real-time processing issues, and data bias. Big data applications improve election forecasting, campaign strategies, and voter survey precision. This work highlights how political science aligns with AI and data science to build accurate, technology-enabled election prediction models. It offers essential tools and methods for researchers, strategists, and policymakers to analyze modern voting behaviour.

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