Political Ideology and Polarization of Policy Positions: A
Multi-dimensional Approach
- URL: http://arxiv.org/abs/2106.14387v1
- Date: Mon, 28 Jun 2021 04:03:04 GMT
- Title: Political Ideology and Polarization of Policy Positions: A
Multi-dimensional Approach
- Authors: Barea Sinno, Bernardo Oviedo, Katherine Atwell, Malihe Alikhani, Junyi
Jessy Li
- Abstract summary: We study the ideology of the policy under discussion teasing apart the nuanced co-existence of stance and ideology.
Aligned with the theoretical accounts in political science, we treat ideology as a multi-dimensional construct.
We showcase that this framework enables quantitative analysis of polarization, a temporal, multifaceted measure of ideological distance.
- Score: 19.435030285532854
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Analyzing political ideology and polarization is of critical importance in
advancing our understanding of the political context in society. Recent
research has made great strides towards understanding the ideological bias
(i.e., stance) of news media along a left-right spectrum. In this work, we take
a novel approach and study the ideology of the policy under discussion teasing
apart the nuanced co-existence of stance and ideology. Aligned with the
theoretical accounts in political science, we treat ideology as a
multi-dimensional construct, and introduce the first diachronic dataset of news
articles whose political ideology under discussion is annotated by trained
political scientists and linguists at the paragraph-level. We showcase that
this framework enables quantitative analysis of polarization, a temporal,
multifaceted measure of ideological distance. We further present baseline
models for ideology prediction.
Related papers
- 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) - 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) - Understanding Divergent Framing of the Supreme Court Controversies:
Social Media vs. News Outlets [56.67097829383139]
We focus on the nuanced distinctions in framing of social media and traditional media outlets concerning a series of U.S. Supreme Court rulings.
We observe significant polarization in the news media's treatment of affirmative action and abortion rights, whereas the topic of student loans tends to exhibit a greater degree of consensus.
arXiv Detail & Related papers (2023-09-18T06:40:21Z) - Social media polarization reflects shifting political alliances in
Pakistan [44.99833362998488]
Spanning from 2018 to 2022, our analysis of Twitter data allows us to capture pivotal shifts and developments in Pakistan's political arena.
By examining interactions and content generated by politicians affiliated with major political parties, we reveal a consistent and active presence of politicians on Twitter.
Our analysis also uncovers significant shifts in political affiliations, including the transition of politicians to the opposition alliance.
arXiv Detail & Related papers (2023-09-15T00:07:48Z) - 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) - PAR: Political Actor Representation Learning with Social Context and
Expert Knowledge [45.215862050840116]
We propose textbfPAR, a textbfPolitical textbfActor textbfRepresentation learning framework.
We retrieve and extract factual statements about legislators to leverage social context information.
We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations.
arXiv Detail & Related papers (2022-10-15T19:28:06Z) - Aggression and "hate speech" in communication of media users: analysis
of control capabilities [50.591267188664666]
Authors studied the possibilities of mutual influence of users in new media.
They found a high level of aggression and hate speech when discussing an urgent social problem - measures for COVID-19 fighting.
Results can be useful for developing media content in a modern digital environment.
arXiv Detail & Related papers (2022-08-25T15:53:32Z) - Detecting Extreme Ideologies in Shifting Landscapes: an Automatic &
Context-Agnostic Approach [7.197469507060225]
This work presents an end-to-end ideology detection pipeline applicable to large-scale datasets.
We construct context-agnostic and automatic ideological signals from widely available media slant data.
We employ the pipeline for left-right ideology, and (the more concerning) detection of extreme ideologies.
arXiv Detail & Related papers (2022-08-08T12:31:33Z) - 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) - Encoding Heterogeneous Social and Political Context for Entity Stance
Prediction [7.477393857078695]
We propose the novel task of entity stance prediction.
We retrieve facts from Wikipedia about social entities regarding contemporary U.S. politics.
We then annotate social entities' stances towards political ideologies with the help of domain experts.
arXiv Detail & Related papers (2021-08-09T08:59:43Z)
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.