The "Right" Discourse on Migration: Analysing Migration-Related Tweets in Right and Far-Right Political Movements

• URL: http://arxiv.org/abs/2510.21220v1
• Date: Fri, 24 Oct 2025 07:45:18 GMT
• Title: The "Right" Discourse on Migration: Analysing Migration-Related Tweets in Right and Far-Right Political Movements
• Authors: Nishan Chatterjee, Veronika Bajt, Ana Zwitter Vitez, Senja Pollak,
• Abstract summary: We propose a methodology to analyse the MIGR-TWIT corpus of far-right tweets in English and French.<n>We aim to uncover patterns of discourse surrounding migration, hate speech, and persuasion techniques employed by right and far-right actors.
• Score: 1.9744211364731628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of right-wing populism in Europe has brought to the forefront the significance of analysing social media discourse to understand the dissemination of extremist ideologies and their impact on political outcomes. Twitter, as a platform for interaction and mobilisation, provides a unique window into the everyday communication of far-right supporters. In this paper, we propose a methodology that uses state-of-the-art natural language processing techniques with sociological insights to analyse the MIGR-TWIT corpus of far-right tweets in English and French. We aim to uncover patterns of discourse surrounding migration, hate speech, and persuasion techniques employed by right and far-right actors. By integrating linguistic, sociological, and computational approaches, we seek to offer cross-disciplinary insights into societal dynamics and contribute to a better understanding of contemporary challenges posed by right-wing extremism on social media platforms.

Related papers

• When Do Language Models Endorse Limitations on Human Rights Principles? [82.84306700922664]
  We evaluate how Large Language Models (LLMs) navigate trade-offs involving the Universal Declaration of Human Rights (UDHR)<n>Our analysis of eleven major LLMs reveals systematic biases where models accept limiting Economic, Social, and Cultural rights more often than Political and Civil rights.
  arXiv  Detail & Related papers  (2026-03-04T16:01:53Z)
• Integrating Large Language Models and Knowledge Graphs to Capture Political Viewpoints in News Media [3.509771256227556]
  We develop a pipeline to identify the range of viewpoints expressed about a given topic.<n>We also enrich claim representations with semantic descriptions drawn from Wikidata.<n>We evaluate our approach against alternative solutions on a benchmark centred on the UK immigration debate.
  arXiv  Detail & Related papers  (2025-12-16T20:10:55Z)
• Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
  We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
  arXiv  Detail & Related papers  (2025-10-16T20:30:20Z)
• Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMs [0.3437656066916039]
  We explore how large language models assess offensiveness in political discourse when prompted to adopt specific political and cultural perspectives.<n>Using a multilingual subset of the MD-Agreement dataset centered on tweets from the 2020 US elections, we evaluate several recent LLMs.<n>Our results show that larger models with explicit reasoning abilities are more consistent and sensitive to ideological and cultural variation.
  arXiv  Detail & Related papers  (2025-09-27T15:20:44Z)
• Talking Point based Ideological Discourse Analysis in News Events [62.18747509565779]
  We propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.<n>Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion.<n>We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation.
  arXiv  Detail & Related papers  (2025-04-10T02:52:34Z)
• "Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection [5.277598111323805]
  Existing approaches to classifying ideology are limited in that they require extensive human effort, the labeling of large datasets, and are not able to adapt to evolving ideological contexts.<n>This paper explores the potential of Large Language Models for classifying the political ideology of online content in the context of the two-party US political spectrum through in-context learning.
  arXiv  Detail & Related papers  (2025-03-23T02:32:25Z)
• Political-LLM: Large Language Models in Political Science [159.95299889946637]
  Large language models (LLMs) have been widely adopted in political science tasks.<n>Political-LLM aims to advance the comprehensive understanding of integrating LLMs into computational political science.
  arXiv  Detail & Related papers  (2024-12-09T08:47:50Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language.<n>This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• 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)
• The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
  We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.<n>We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
  arXiv  Detail & Related papers  (2023-04-19T18:32:49Z)
• 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)
• 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)
• Political Ideology and Polarization of Policy Positions: A Multi-dimensional Approach [19.435030285532854]
  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.
  arXiv  Detail & Related papers  (2021-06-28T04:03:04Z)

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.