Artificial Intelligence for Election Result Prediction and Turnout Forecasting: A Systematic Review of Literature (2020–2025)

Vollständiger Abstract

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This is evidence that artificial intelligence (AI) has become a new paradigm in the area of electoral forecasting. As computational power and sets have grown exponentially, AI-powered models provide a more dynamic and data-intensive approach compared to conventional polling and statically methods. This review looks at the period from 2020 to 2025, based on a systematic search of major academic databases including IEEE Xplore, ACM Digital Library, ScienceDirect, PubMed and Google Scholar, which identified 63 studies. Through a lens based on public policy, the study explores how AI is transforming political analytics and decision-making with a focus on purpose-built applications for predicting election results and forecasting voter turnout. Within the reviewed literature, a variety of AI approaches are employed to further improve predictive performance. However, traditional machine learning algorithms (Random Forests, Support Vector Machines and Gradient Boosting) have been frequently used for structured electoral data and socio-demographic information while deep learning protocols (e.g., Long Short-Term Memory Networks and Transformer architectures), are able to capture temporal voting trends as well as intricate relationships. Furthermore, natural language processing methods like sentiment analysis and topic modeling are utilized for extracting public opinion from social media sites in order to gain real-time insights of voter’s behavior. For example, sentiment trends on platforms like Twitter have been used to approximate voter inclination during election cycles, while historical voting and economic indicators improve turnout predictions. Although these models achieve high accuracy in politically stable environments, their performance tends to decline in volatile contexts where misinformation, sudden political shifts, or imbalanced datasets distort predictions. Despite the promise of AI in electoral forecasting, several critical challenges persist. Issues such as algorithmic bias can skew predictions against certain demographic groups, while data privacy concerns arise from the use of personal and social media information. Furthermore, the “blacbox” nature of many AI models limits interpretability, making it difficult for stakeholders to fully trust or understand predictions. Another key limitation is the lack of cross-national generalizability, as models trained in one political context may not perform well in another due to differing electoral systems and sociopolitical dynamics. Moving forward, research must prioritize transparency, fairness, and ethical accountability, ensuring that AI-driven forecasting tools enhance democratic processes rather than undermine them.