Automated Identification of Competing Narratives in Political Discourse on Social Media — Paper

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.

Authors 2

Sergej Wildemann, Erick Elejalde

Specification

Official page: arxiv.org/abs/2609.11202
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
arXiv id: 2609.11202
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Categories: cs.CL, cs.SI
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
PDF: arxiv.org/pdf/2609.11202
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Primary category: cs.CL
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

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Attributed facts

9

Source tiers

T19

Freshest observation

5 h ago

Conflicts

None

No models linked to this paper yet.

Authors: Sergej Wildemann, Erick Elejalde

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Claim history · Official page

1 claims · 1 propertiesShow all properties

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Claim history for Official page
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2609.11202	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperAutomated Identification of Competing Narratives in Political Discourse on Social Media
New paper: Automated Identification of Competing Narratives in Political Discourse on Social Media
arxiv
rss.arxiv.org

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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CL	feed	T1· Official	3 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.