From Digital Data to Electoral Forecasts: A Systematic Review and Taxonomy of Computational Approaches

Abstract

The increasing use of digital data in electoral prediction has motivated a growing body of computational research, yet the field remains methodologically diverse and lacks consolidated comparative frameworks. This article presents a systematic review of computational approaches for electoral outcome prediction using digital data between 2020 and 2025. Following rigorous systematic methodology, searches were conducted across three scientific databases, resulting in 80 primary studies analyzed after applying explicit quality criteria. The review proposes a taxonomy classifying studies by data integration and predictive complexity, enabling systematic identification of methodological patterns. Results reveal geographic concentration in few countries, with Twitter as the dominant platform and sentiment analysis as the most frequent technique. Vote percentage prediction and winner identification represent the primary objectives, evaluated mainly through regression and classification metrics. The field demonstrates numerical expansion with modest geographic diversification, yet persistent challenges remain regarding sample representativeness, cross-context generalization, and absence of standardized validation protocols. Findings indicate the need for broader geographic coverage, reduced platform dependency, and establishment of uniform evaluation criteria to advance methodological maturity in computational electoral prediction.