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
- 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) - Generalizing Political Leaning Inference to Multi-Party Systems:
Insights from the UK Political Landscape [10.798766768721741]
An ability to infer the political leaning of social media users can help in gathering opinion polls.
We release a dataset comprising users labelled by their political leaning as well as interactions with one another.
We show that interactions in the form of retweets between users can be a very powerful feature to enable political leaning inference.
arXiv Detail & Related papers (2023-12-04T09:02:17Z) - 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) - Election Polarization: Mapping citizen divisions through elections [0.21847754147782888]
We examine the concept of Election Polarization as a measure of citizens' divisions on Election Day.
We use both synthetic data and presidential election results from France, Chile, and the United States.
We validate its robustness over the election type, aggregation scale, use of abstentions/spoilt votes, and the number of candidates.
arXiv Detail & Related papers (2023-08-16T18:06:00Z) - 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) - 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.