Semantic Topology: a New Perspective for Communication Style Characterization

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 is more likely to exhibit more complex topological structures, with greater thematic interleaving and longrange coherence, whereas fake news favors simpler, more linear narratives. These findings suggest that topological features capture stylistic distinctions beyond traditional linguistic cues, offering new insights for discourse modeling.