Machine Learning and Statistical Approaches to Measuring Similarity of Political Parties

• URL: http://arxiv.org/abs/2306.03079v1
• Date: Mon, 5 Jun 2023 17:53:41 GMT
• Title: Machine Learning and Statistical Approaches to Measuring Similarity of Political Parties
• Authors: Daria Boratyn, Damian Brzyski, Beata Kosowska-G\k{a}sto{\l}, Jan Rybicki, Wojciech S{\l}omczy\'nski, Dariusz Stolicki
• Abstract summary: Mapping political party systems to metric policy spaces is one of the major methodological problems in political science. We consider how advances in natural language processing, including large transformer-based language models, can be applied to solve that issue.
• Score: 0.4893345190925177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mapping political party systems to metric policy spaces is one of the major methodological problems in political science. At present, in most political science project this task is performed by domain experts relying on purely qualitative assessments, with all the attendant problems of subjectivity and labor intensiveness. We consider how advances in natural language processing, including large transformer-based language models, can be applied to solve that issue. We apply a number of texts similarity measures to party political programs, analyze how they correlate with each other, and -- in the absence of a satisfactory benchmark -- evaluate them against other measures, including those based on expert surveys, voting records, electoral patterns, and candidate networks. Finally, we consider the prospects of relying on those methods to correct, supplement, and eventually replace expert judgments.

Related papers

• The Impact of Persona-based Political Perspectives on Hateful Content Detection [4.04666623219944]
  Politically diverse language models require computational resources often inaccessible to many researchers and organizations. Recent work has established that persona-based prompting can introduce political diversity in model outputs without additional training. We investigate whether such prompting strategies can achieve results comparable to political pretraining for downstream tasks.
  arXiv  Detail & Related papers  (2025-02-01T09:53:17Z)
• Political-LLM: Large Language Models in Political Science [159.95299889946637]
  Large language models (LLMs) have been widely adopted in political science tasks. Political-LLM aims to advance the comprehensive understanding of integrating LLMs into computational political science.
  arXiv  Detail & Related papers  (2024-12-09T08:47:50Z)
• Evaluating Generative AI Systems is a Social Science Measurement Challenge [78.35388859345056]
  We present a framework for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems. The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves.
  arXiv  Detail & Related papers  (2024-11-17T02:35:30Z)
• A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
  stance detection has emerged as a crucial subfield within affective computing. Recent years have seen a surge of research interest in developing effective stance detection methods. This paper provides a comprehensive survey of stance detection techniques on social media.
  arXiv  Detail & Related papers  (2024-09-24T03:06:25Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• L(u)PIN: LLM-based Political Ideology Nowcasting [1.124958340749622]
  We present a method to analyze ideological positions of individual parliamentary representatives by leveraging the latent knowledge of LLMs. The method allows us to evaluate the stance of politicians on an axis of our choice allowing us to flexibly measure the stance of politicians in regards to a topic/controversy of our choice.
  arXiv  Detail & Related papers  (2024-05-12T16:14:07Z)
• Modelling Political Coalition Negotiations Using LLM-based Agents [53.934372246390495]
  We introduce coalition negotiations as a novel NLP task, and model it as a negotiation between large language model-based agents. We introduce a multilingual dataset, POLCA, comprising manifestos of European political parties and coalition agreements over a number of elections in these countries. We propose a hierarchical Markov decision process designed to simulate the process of coalition negotiation between political parties and predict the outcomes.
  arXiv  Detail & Related papers  (2024-02-18T21:28:06Z)
• Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies [5.958974943807783]
  This study addresses the challenge of understanding political bias in digitized discourse using Large Language Models (LLMs) We present a comprehensive analytical framework, consisting of Partisan Bias Divergence Assessment and Partisan Class Tendency Prediction. Our findings reveal the model's effectiveness in capturing emotional and moral nuances, albeit with some challenges in stance detection.
  arXiv  Detail & Related papers  (2023-11-16T08:57:53Z)
• Examining Political Rhetoric with Epistemic Stance Detection [13.829628375546568]
  We develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. We demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books.
  arXiv  Detail & Related papers  (2022-12-29T23:47:14Z)
• Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation [60.71312668265873]
  We develop a method to balance the need for personalization with confident predictions. We show that our method can be used to form accurate predictions of heterogeneous treatment effects.
  arXiv  Detail & Related papers  (2021-11-28T23:19:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.