Semantic Topology: a New Perspective for Communication Style Characterization (2025)

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.

## introduction recent years have witnessed increasing intersections between bioinformatics and natural language processing, spanning applications such as phylogenetic trees @xcite , text mining for automated information retrieval @xcite , network analysis @xcite , and bio-inspired computational models for language evolution @xcite . however, many opportunities remain unexplored, particularly with the growing availability of data and machine learning techniques. one such opportunity involves leveraging topology, originally used to describe the three-dimensional structure of biological macromolecules, to analyze textual structure. specifically, we propose applying the circuit topology (ct) framework @xcite @xcite @xcite to quantify the arrangement of semantic "contacts" within a sequence of sentences. in biology, ct models how loops formed by intrachain contacts influence macromolecular function; in language, these loops correspond to semantic recurrences, representing the re-introduction of a theme or topic. our work builds on the idea that repeated or closely-related concepts form the backbone of coherent discourse. by treating each sentence as a node and linking sentences with high semantic similarity, we can apply ct to capture how themes are repeated, interleaved, or nested (figure 1 ). this yields topological descriptors-series, parallel, and cross-whose relative prevalence can characterize a text's rhetorical structure. additionally, we introduce the globularity score as a means of weighting these topological features by the distance over which a semantic connection spans. as a case study, we perform semantic topological analysis on two datasets, the kaggle fake news dataset foot_0 and the fake news corpus @xcite . fake news manipulates public opinion purposefully, often with profound political and social consequences. although many linguistic features have been suggested for fake news extraction, it is still unclear which ones are the most meaningful @xcite . here, we characterize these articles in topology space, and observe statistical differences in the semantic arrangement of true and fake news. we demonstrate how topological descriptors highlight characteristic stylistic patterns in long-form true and fake news (extended narratives composed by at least 15 sentences). all code used for the analysis outlined in this paper is freely available on github foot_1 . ## previous work discourse analysis has long sought to characterize how concepts and arguments are structured within a text. early influential frameworks, such as rhetorical structure theory (rst) @xcite , break down discourse into elementary discourse units and analyze how these units connect via rhetorical relations. subsequent approaches, such as argumentation mining (am) @xcite have focused on the detection of argumentative discourse units (adus), such as claims, premises, and conclusion structures. in this framework, adus can have relationships of different kinds, e.g., support or attack. similarly related work in coherence modeling @xcite has used features like entity grids or lexical chains to capture patterns of entity distribution in a text and coherence assessment. in parallel, the rise of distributional and embeddingbased paradigms of semantics have offered new opportunities for automated discourse exploration. topic modeling methods @xcite @xcite cluster semantically similar passages or documents, thereby allowing for the identification of high-level themes within texts. topological frameworks have also been previously applied for the modeling of language structure, although these efforts remain sporadic. for instance, @xcite represent text as a "small-world" network of textual units, revealing structural properties such as clustering or centrality. moreover, related studies in topological data analysis (tda) @xcite have investigated persistent homology of text features, identifying semantic loops within documents. misinformation detection represents a particularly appealing application for discourse analysis. although many approaches have thus far prioritized lexical or stylistic cues @xcite , sentiment features @xcite , or source reliability, several recent studies have demonstrated the value of rhetorical structure for fake news detection. for instance, @xcite incorporate rst-based features (bag-of-rst) to detect fake news in russian, @xcite introduce a hierarchical discourse-level structure that outperforms prior baselines, and @xcite exploit both contextual and discourse-level information to improve claim classification and fact-checking. work by @xcite and @xcite likewise show that coherence relations and nuclearity can highlight differences between deceptive from truthful documents. ## discussion here, we propose semantic topology as a candidate for discourse analysis. we found that semantic topology highlights statistical differences in longform true and fake news, with complex topologies that entail semantic alternation (parallel and cross) being more prevalent in the former. these findings are in line with previous research indicating fake news as a simpler form of communication @xcite , although the topological complexity of semantic arrangement identifies a different level of analysis with respect to syntax and word choice. hopping between different semantic clusters seems to happen less frequently in fake news articles. however, evidence for this particular effect is weak, and it requires further investigation of what other stylistic choices influence the rate of hopping between semantic clusters. personal perception of semantic similarity is a key concept in the cognitive behavioral sciences @xcite , where it was proven to correlate with personality traits @xcite , and to participate in false memory generation @xcite . in consideration of the findings presented above, there is reason to investigate whether the arrangement of semantically similar units (sentences) in a text might contribute to the psychological allure of fake news, as well as other cognitive phenomena involving effective communication. the semantic topology framework, as introduced in this paper, represents a novel approach rooted in quantitative, data-driven modeling rather than the qualitative categories and relations typical of classical discourse theory. establishing a direct correspondence between circuit topology (ct) relations and classical discourse frameworks is therefore not straightforward. however, certain patterns of semantic arrangements may be indirectly captured by semantic topology. for example, topic shifting in discourse might manifest as series (s) relations within our framework, where thematically distinct units follow one another without recurrence. similarly, semantic merging could be represented by transitions from predominantly series topologies toward more parallel (p) and cross (x) structures, indicating increasing thematic interleaving. moreover, classical discourse frameworks often aim to identify what we refer to as semantic contacts-recurrences of meaning across a text. relations such as restatement, summary, and evaluation, as framed within rhetorical structure theory (rst), might correspond to 'semantic contacts' in the semantic topology framework. however, our method focuses on capturing the pairwise arrangement of these contacts, offering a complementary perspective. while the model presented does not yet capture the full spectrum of semantic structuring described in discourse theory, it introduces a promising higher-order analytical dimension that warrants further exploration. ## conclusion in this paper, we introduce semantic topology as a novel approach to discourse analysis, leveraging the circuit topology framework from bioinformatics. by mapping and quantifying sentence-level semantic relationships, we reveal significant differences in how true and fake news structure recurring themes. our findings suggest that fake news follows simpler topological patterns, aligning with prior research on deceptive discourse. the globularity score and topological descriptors highlight the role of textual loops in coherence and complexity. future work should explore interactions with other discourse frameworks, broader text types, and the cognitive significance of topological relationships. additionally, this method offers potential insights into communication style variations across lifespan development and neurological conditions.