Candidate vote prediction in open-list systems : Forecasting the results of the 2023 Finnish parliamentary election - Vepsäläinen, Tapio (Elsevier, 2026)
The availability of rich online data has opened new opportunities for election forecasting. While typical election forecasting predicts results at the national level, the accumulation of information on candidate and voter behavior enables making predictions on a more granular level. Most studies using online data focus on contests with a small number of candidates, leaving a research gap for elections with larger candidate pools. Elections with numerous candidates differ from races with a limited number of candidates, as voters are more inclined to use heuristics and mental shortcuts when selecting their preferred candidate. Building on this insight, this paper introduces a model to predict each candidate’s vote share in the context of Finnish parliamentary elections. An ex ante forecast based on the model was published before the 2023 Finnish parliamentary election, which correctly identified 150 of the 200 candidates elected to parliament from a total pool of 2468 contestants. The results showcase the potential to effectively leverage the rich online data environment, thus complementing existing methodologies. Compared to traditional approaches, the proposed model provides candidate-level estimates, which offer insights into intra-party competition and list rankings.