Semantic Topology: a New Perspective for Communication Style Characterization

Barbara Scalvini, Alireza Mashaghi


Abstract
We introduce semantic topology, a novel framework for discourse analysis that leverages Circuit Topology to quantify the semantic arrangement of sentences in a text. By mapping recurring themes as series, parallel, or cross relationships, we identify statistical differences in communication patterns in long-form true and fake news. Our analysis of large-scale news datasets reveals that true news are more likely to exhibit more complex topological structures, with greater thematic interleaving and long-range coherence, whereas fake news favor simpler, more linear narratives. These findings suggest that topological features capture stylistic distinctions beyond traditional linguistic cues, offering new insights for discourse modeling.
Anthology ID:
2025.findings-acl.479
Volume:
Findings of the Association for Computational Linguistics: ACL 2025
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9223–9233
Language:
URL:
https://preview.aclanthology.org/display_plenaries/2025.findings-acl.479/
DOI:
Bibkey:
Cite (ACL):
Barbara Scalvini and Alireza Mashaghi. 2025. Semantic Topology: a New Perspective for Communication Style Characterization. In Findings of the Association for Computational Linguistics: ACL 2025, pages 9223–9233, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
Semantic Topology: a New Perspective for Communication Style Characterization (Scalvini & Mashaghi, Findings 2025)
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PDF:
https://preview.aclanthology.org/display_plenaries/2025.findings-acl.479.pdf