With respect to each of the above pillars,
1. Advances in Socio-Semantic Network Analysis
Focusing on online communities, the project contributed significantly to socio-semantic network analysis, a field that examines the interaction between social structures and semantic meaning, both:
- empirically: Socsemics analyzed how social groups align with semantic categories, particularly in relation to ideological divides (e.g. left vs. right-wing), stance on controversial issues (e.g. climate skeptics vs. non-skeptics), and geographical location (e.g. intra-EU vs. intercontinental). Contrary to common assumptions, the research found that not all online communities function as echo chambers. While affiliation-based networks (e.g. Twitter retweets) tend to resemble echo chambers, interaction-based networks (e.g. Twitter quotes) often display greater ideological diversity. This suggests that the nature of online engagement plays a crucial role in shaping discourse.
- methodologically: the project extended stochastic block modeling (SBM) to analyze social and semantic clustering beyond a single metadata category (e.g. political affiliation or topic preferences).
A habilitation manuscript and two book chapters further contributed to establish the sociological and computational relevance of combining social and semantic network analysis.
2. Advances in Computational Linguistics and Opinion Representation
To analyze user opinions in digital spaces, the project introduced Semantic Hypergraphs (SHs)—a novel framework for representing sentence-level meaning through directed, recursive hyperedges. Originally developed to categorize user stances on social media, SHs proved more relevant for semi-supervised information retrieval, particularly in contexts requiring rigorous, transparent interpretable methods—offering an alternative to large language models (LLMs), which often lack transparency. It allows efficient pattern-based extraction of structured information, enabling human operators to refine queries with minimal effort; and it configures an alternative to traditional semantic graphs, with potential commercial applications, particularly for companies requiring cost-effective, customizable information retrieval solutions. Recognition of its potential has led to an early-stage €150k application grant (starting in 2025) and an ERC Proof-of-Concept resubmission after earning a Seal of Excellence.
3. Advances in Socio-Semantic Network Visualization
The project developed novel ways to visualize social network structures alongside semantic properties by representing social and semantic elements in a unified hybrid visualization, mapping social clusters while considering semantic properties, revealing whether cohesive groups share similar semantics or not; and developing an interactive platform to assess socio-semantic fragmentation, showing how connections (or their absence) reflect structural and semantic patterns. Additionally, a new user sampling method based on structural modeling, rather than demographic quotas, was implemented. Combined with an augmented interview protocol (where users engage with visualizations of their network position), this approach represents a novel method in socio-semantic research.
Besides, three key software developments can be noted:
- graphbrain (github.com/graphbrain) an open-source Python library for semantic hypergraph analysis, that was used primarily within the project to extract structured claims from online discussions.
- metablox (github.com/lenafm/metablox) a stochastic block modeling tool for analyzing how categorical metadata (e.g. political affiliations) shape social networks.
- chronoblox (github.com/lobbeque/chronoblox) a visualization tool integrating structural and semantic properties over time, introducing "network chronophotography" for tracking dynamic changes.
