PopBERT. Detecting populism and its host ideologies in the German Bundestag

• URL: http://arxiv.org/abs/2309.14355v1
• Date: Fri, 22 Sep 2023 14:48:02 GMT
• Title: PopBERT. Detecting populism and its host ideologies in the German Bundestag
• Authors: L. Erhard, S. Hanke, U. Remer, A. Falenska and R. Heiberger
• Abstract summary: This paper aims to provide a reliable, valid, and scalable approach to measure populist stances. We label moralizing references to the virtuous people or the corrupt elite as core dimensions of populist language. To identify, in addition to how the thin ideology of populism is thickened, we annotate how populist statements are attached to left-wing or right-wing host ideologies.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of populism concerns many political scientists and practitioners, yet the detection of its underlying language remains fragmentary. This paper aims to provide a reliable, valid, and scalable approach to measure populist stances. For that purpose, we created an annotated dataset based on parliamentary speeches of the German Bundestag (2013 to 2021). Following the ideational definition of populism, we label moralizing references to the virtuous people or the corrupt elite as core dimensions of populist language. To identify, in addition, how the thin ideology of populism is thickened, we annotate how populist statements are attached to left-wing or right-wing host ideologies. We then train a transformer-based model (PopBERT) as a multilabel classifier to detect and quantify each dimension. A battery of validation checks reveals that the model has a strong predictive accuracy, provides high qualitative face validity, matches party rankings of expert surveys, and detects out-of-sample text snippets correctly. PopBERT enables dynamic analyses of how German-speaking politicians and parties use populist language as a strategic device. Furthermore, the annotator-level data may also be applied in cross-domain applications or to develop related classifiers.

Related papers

• 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)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• Scaling Political Texts with Large Language Models: Asking a Chatbot Might Be All You Need [0.0]
  We use instruction-tuned Large Language Models (LLMs) to position political texts within policy and ideological spaces. We illustrate and validate the approach by scaling British party manifestos on the economic, social, and immigration policy dimensions. The correlation between the position estimates obtained with the best LLMs and benchmarks based on coding by experts, crowdworkers or roll call votes exceeds.90.
  arXiv  Detail & Related papers  (2023-11-28T09:45:02Z)
• Voting Booklet Bias: Stance Detection in Swiss Federal Communication [0.0]
  We use recent stance detection methods to study the stance (for, against or neutral) of statements in official information booklets for voters. Our main goal is to answer the fundamental question: are topics to be voted on presented in a neutral way? Our findings have implications for the editorial process of future voting booklets and the design of better automated systems for analyzing political discourse.
  arXiv  Detail & Related papers  (2023-06-15T09:49:12Z)
• The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [57.70351255180495]
  We apply a deep-learning-based computer-vision algorithm to a sample of 220 YouTube videos depicting political leaders from 15 different countries. We observe statistically significant differences in the average score of expressed negative emotions between groups of leaders with varying degrees of populist rhetoric.
  arXiv  Detail & Related papers  (2023-04-19T18:32:49Z)
• ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning [0.0]
  This paper assesses the accuracy, reliability and bias of the Large Language Model (LLM) ChatGPT-4 on the text analysis task of classifying the political affiliation of a Twitter poster based on the content of a tweet. We use Twitter messages from United States politicians during the 2020 election, providing a ground truth against which to measure accuracy.
  arXiv  Detail & Related papers  (2023-04-13T14:51:40Z)
• The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections [12.890022659051557]
  This study compares how populist parties advertised on Facebook during the European 2019 Parliamentary election. We examine commonalities and differences in which audiences they reach and on which issues they focus. We find that populist parties are more likely to focus on monetary policy, state bureaucracy and reforms, and security.
  arXiv  Detail & Related papers  (2023-02-08T13:22:11Z)
• Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
  We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
  arXiv  Detail & Related papers  (2023-01-02T12:40:05Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty in Election Polls [56.8172499765118]
  We discuss potential sources of bias in nowcasting and forecasting. Concepts are presented to attenuate the issue of falsely perceived accuracy. One key idea is the use of Probabilities of Events instead of party shares.
  arXiv  Detail & Related papers  (2021-04-28T07:02:24Z)
• Text-Based Ideal Points [26.981303055207267]
  We introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors. The TBIP separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. It can estimate ideal points of anyone who authors political texts, including non-voting actors.
  arXiv  Detail & Related papers  (2020-05-08T21:16:42Z)

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.