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Ansia Dibuja, D., Folgado, M. G., & Sanz, V. (2026). Analyzing polarization among Spanish political elites using machine learning techniques. J. Comput. Soc. Sci., 9(1), 4–26pp.
Abstract: This study analyzes ideological and affective polarisation in the Spanish Parliament from 2000 to 2022 using Natural Language Processing (NLP) techniques. Parliamentary records were harvested, pre-processed, and analyzed with document embeddings to assess ideological polarisation, and with sentiment analysis models (VADER and Transformer-based) to measure affective polarisation. The findings reveal a significant increase in both ideological and affective divisions, particularly in recent legislative terms. This research contributes new tools for mapping political discourse and provides a rich, publicly available dataset to support further studies on Spanish political elites.
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Folgado, M. G., & Sanz, V. (2022). Exploring the political pulse of a country using data science tools. J. Comput. Soc. Sci., 5, 987–1000.
Abstract: In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis.
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