Automated Identification of Competing Narratives in Political Discourse on Social Media

• URL: http://arxiv.org/abs/2609.11202v1
• Date: Thu, 10 Sep 2026 08:10:33 GMT
• Title: Automated Identification of Competing Narratives in Political Discourse on Social Media
• Abstract summary: This paper presents an unsupervised framework for identifying and characterizing competing narratives in political discourse on social media.<n>The framework is able to detect the key competing narratives, highlighting the divergent framings and conflicts surrounding trending political topics.<n>The findings contribute to the broader understanding of how narratives propagate within the digital public sphere.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media platforms have become central to shaping political discourse, serving as arenas where narratives form and evolve, influencing public opinion. Identifying and analyzing these narratives, particularly when they compete across different political ideologies, is crucial for understanding the dynamics of modern political communication. This paper presents an unsupervised framework for identifying and characterizing competing narratives in political discourse on social media, focusing on German politicians' tweets. The framework employs a multi-stage pipeline that integrates natural language processing techniques such as topic modeling, event detection, and event linking. By forming data into coherent stories and uncovering the distinct perspectives of user communities, the system is able to detect the key competing narratives, highlighting the divergent framings and conflicts surrounding trending political topics. Two case studies on polarizing political issues demonstrate the efficacy of the methodology, showcasing its ability to uncover and analyze divergent viewpoints. The findings contribute to the broader understanding of how narratives propagate within the digital public sphere and offer insights for policymakers, social media platforms, and researchers interested in monitoring political discourse.

Related papers

• Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media [17.163713154720806]
  TSN4PI is a unified framework for tracking the evolution of political ideologies on social media.<n>The PIDN uses large language models with style transfer and unsupervised domain adaptation to enable robust ideology detection.<n>The PIPN employs temporal graph neural networks to predict future ideological shifts.
  arXiv  Detail & Related papers  (2026-08-18T16:33:32Z)
• 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)
• Conflicting narratives and polarization on social media [0.0]
  We show how the analysis of conflicting narratives provides insight into the discursive mechanisms of polarization and issue alignment in the public sphere.<n>Building upon previous work that has identified ideologically polarized issues in the German Twittersphere between 2021 and 2023, we analyze the discursive dimension of polarization.<n>We show evidence for conflicting narratives along two dimensions: (i) different attributions of actantial roles to the same set of actants, and (ii) emplotment of different actants for the same event.
  arXiv  Detail & Related papers  (2025-07-21T13:22:57Z)
• Unveiling Political Influence Through Social Media: Network and Causal Dynamics in the 2022 French Presidential Election [0.0]
  During the 2022 French presidential election, we collected daily Twitter messages on key topics posted by political candidates and their close networks.<n>Using a data-driven approach, we analyze interactions among political parties, identifying central topics that shape the landscape of political debate.<n>Our findings demonstrate how specific issues, such as health and foreign policy, act as catalysts for cross-party influence, particularly during critical election phases.
  arXiv  Detail & Related papers  (2025-06-19T16:35:02Z)
• 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)
• 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)
• Extracting narrative signals from public discourse: a network-based approach [2.320417845168326]
  We propose a graph-based formalism and machine-guided method for extracting, representing, and analyzing selected narrative signals.<n>We study political narratives that figure in texts from digital media such as archived political speeches, social media posts, transcripts of parliamentary debates, and political manifestos on party websites.
  arXiv  Detail & Related papers  (2024-11-01T16:05:59Z)
• A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
  stance detection has emerged as a crucial subfield within affective computing. Recent years have seen a surge of research interest in developing effective stance detection methods. This paper provides a comprehensive survey of stance detection techniques on social media.
  arXiv  Detail & Related papers  (2024-09-24T03:06:25Z)
• Modeling Political Orientation of Social Media Posts: An Extended Analysis [0.0]
  Developing machine learning models to characterize political polarization on online social media presents significant challenges. These challenges mainly stem from various factors such as the lack of annotated data, presence of noise in social media datasets, and the sheer volume of data. We introduce two methods that leverage on news media bias and post content to label social media posts. We demonstrate that current machine learning models can exhibit improved performance in predicting political orientation of social media posts.
  arXiv  Detail & Related papers  (2023-11-21T03:34:20Z)
• 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)
• 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)

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.