Prismix
arXiv — NLP / Computation & Language · · 3 min read

Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse

#paper

Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.

Like Read original ↗

Computer Science > Social and Information Networks

arXiv:2605.21540 (cs)
[Submitted on 20 May 2026]

Title:Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse

View a PDF of the paper titled Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse, by Despoina Antonakaki and 1 other authors
View PDF HTML (experimental)
Abstract:The proliferation of large language models has introduced a new paradigm of synthetic political communication in which narratives may be generated, semantically coordinated, and strategically disseminated across platforms at scale. We present a cross-platform framework for detecting synthetic political narratives using four coordination signals -- lexical diversity D(C), temporal burstiness B(C), rhetorical repetition R(C), and semantic homogenization H(C) -- combined into a Synthetic Narrative Coordination Score SNC(C).
We apply the framework to a corpus of 353,223 records spanning six geopolitical event windows collected from six Telegram channels and nine Reddit communities (2023--2026). Results show that IntelSlava exhibits the lowest lexical diversity (MATTR 0.52--0.54), the highest burstiness (B=+0.48 to +0.73), and the highest rhetorical overlap with peer channels (Jaccard 0.12), ranking first in the composite SNC(C) on four of six event windows (SNC 0.45--0.60). Rybar ranks last on all windows despite its high semantic homogenization, because its Russian-language output yields high lexical diversity and near-zero rhetorical Jaccard with English-language channels -- demonstrating that no single indicator is sufficient for coordination detection. Multi-dimensional SNC(C) scoring provides a more robust and interpretable signal than any individual metric.
Subjects: Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
Cite as: arXiv:2605.21540 [cs.SI]
  (or arXiv:2605.21540v1 [cs.SI] for this version)
  https://doi.org/10.48550/arXiv.2605.21540
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Despoina Antonakaki [view email]
[v1] Wed, 20 May 2026 07:58:49 UTC (12,070 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse, by Despoina Antonakaki and 1 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source

Current browse context:

cs.SI
< prev   |   next >
Change to browse by:

References & Citations

Loading...

BibTeX formatted citation

loading...
Data provided by:

Bookmark

BibSonomy Reddit
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from arXiv — NLP / Computation & Language