2023/12/02 by Alexander Kolpakov, Kolpakov, Alexander, Igor Rivin +1
Social Sciences · #American Constitutional Law and Politics #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #H.3.1 #H.3.3 #I.2.7 #I.5.4 #Machine Learning (cs.LG) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2312.01185
openalex publication_date 2023/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this technical note we suggest a novel approach to discover temporal (related and unrelated to language dilation) and personality (authorship attribution) aspects in historical datasets. We exemplify our approach on the State of the Union addresses given by the past 42 US presidents: this dataset is known for its relatively small amount of data, and high variability of the size and style of texts. Nevertheless, we manage to achieve about 95% accuracy on the authorship attribution task, and pin down the date of writing to a single presidential term.