Predicting Partisan Responsiveness: A Probabilistic Text Mining Time-Series Approach

Abstract

When do parties respond to their political rivals and when do they ignore them? This article presents a new computational framework to detect, analyze and predict partisan responsiveness by showing when parties on opposite poles of the political spectrum react to each other’s agendas and thereby contribute to polarization. Once spikes in responsiveness are detected and categorized using latent Dirichlet allocation, we utilize the terms that comprise the topics, together with a gradient descent solver, to assess the classifier’s predictive accuracy. Using 10,597 documents from the official websites of radical right and ethnic political parties in Slovakia (2004–2014), the analysis predicts which political issues will elicit partisan reactions, and which will be ignored, with an accuracy of 83% (F-measure) and outperforms both Random Forest and Naive Bayes classifiers. Subject matter experts validate the approach and interpret the results.

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“…Whereas previous literature (Pirro 2014;Minkenberg 2015;Pytlas 2015;Bustikova 2019) explored the politicisation of ethnic group rights and its implication for political polarisation, this research focuses predominantly on traditional groups (Roma, Jews, various ethnic minorities) and domestic dynamics. We build on this literature by shifting the focus to a new groups as new sources of polarisation (Bustikova 2014(Bustikova , 2017(Bustikova , 2019Bustikova et al 2019). Furthermore, we incorporate the potential effects of the transnational legal framework in shaping the dynamics of domestic party competition (Guasti 2017(Guasti , 2018(Guasti , 2019Guasti, Siroky, and Stockemer 2017).…”