Understanding Political Divisiveness using Online Participation data from the 2022 French and Brazilian Presidential Elections

• URL: http://arxiv.org/abs/2211.04577v2
• Date: Wed, 25 Oct 2023 14:41:49 GMT
• Title: Understanding Political Divisiveness using Online Participation data from the 2022 French and Brazilian Presidential Elections
• Authors: Carlos Navarrete, Mariana Macedo, Rachael Colley, Jingling Zhang, Nicole Ferrada, Maria Eduarda Mello, Rodrigo Lira, Carmelo Bastos-Filho, Umberto Grandi, Jerome Lang, C\'esar A. Hidalgo
• Abstract summary: We present data collected in an online experiment where participants built personalized government programs. We find that a metric of divisiveness, which is uncorrelated with traditional aggregation functions, can identify polarizing proposals.
• Score: 6.021640769621497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital technologies can augment civic participation by facilitating the expression of detailed political preferences. Yet, digital participation efforts often rely on methods optimized for elections involving a few candidates. Here we present data collected in an online experiment where participants built personalized government programs by combining policies proposed by the candidates of the 2022 French and Brazilian presidential elections. We use this data to explore aggregates complementing those used in social choice theory, finding that a metric of divisiveness, which is uncorrelated with traditional aggregation functions, can identify polarizing proposals. These metrics provide a score for the divisiveness of each proposal that can be estimated in the absence of data on the demographic characteristics of participants and that explains the issues that divide a population. These findings suggest divisiveness metrics can be useful complements to traditional aggregation functions in direct forms of digital participation.

Related papers

• ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents [70.17229548653852]
  We introduce ElectionSim, an innovative election simulation framework based on large language models. We present a million-level voter pool sampled from social media platforms to support accurate individual simulation. We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario.
  arXiv  Detail & Related papers  (2024-10-28T05:25:50Z)
• Will Trump Win in 2024? Predicting the US Presidential Election via Multi-step Reasoning with Large Language Models [12.939107088730513]
  Election prediction poses unique challenges, such as limited voter-level data, rapidly changing political landscapes, and the need to model complex human behavior. We introduce a multi-step reasoning framework designed for political analysis. Our approach is validated on real-world data from the American National Election Studies (ANES) 2016 and 2020. We apply our framework to predict the outcome of the 2024 U.S. presidential election in advance.
  arXiv  Detail & Related papers  (2024-10-21T06:18:53Z)
• 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)
• Political Leaning Inference through Plurinational Scenarios [4.899818550820576]
  This work focuses on three diverse regions in Spain (Basque Country, Catalonia and Galicia) to explore various methods for multi-party categorization. We use a two-step method involving unsupervised user representations obtained from the retweets and their subsequent use for political leaning detection.
  arXiv  Detail & Related papers  (2024-06-12T07:42:12Z)
• On the steerability of large language models toward data-driven personas [98.9138902560793]
  Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented. Here, we present a novel approach to achieve controllable generation of specific viewpoints using LLMs.
  arXiv  Detail & Related papers  (2023-11-08T19:01:13Z)
• Mapping Election Polarization and Competitiveness using Election Results [0.18641315013048293]
  We argue that voting patterns can lead to mapping effective proxies of citizen divisions on election day. This paper perspectives two complementary concepts, Election Polarization (EP) and Election Competitiveness (EC) We present an approach that relies solely on election data and validate it using synthetic and real-world election data across 13 countries in the Eurozone, North America, Latin America, and New Zealand.
  arXiv  Detail & Related papers  (2023-08-16T18:06:00Z)
• 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)
• Reaching the bubble may not be enough: news media role in online political polarization [58.720142291102135]
  A way of reducing polarization would be by distributing cross-partisan news among individuals with distinct political orientations. This study investigates whether this holds in the context of nationwide elections in Brazil and Canada.
  arXiv  Detail & Related papers  (2021-09-18T11:34:04Z)
• 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)

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.