Testing network clustering algorithms with natural language processing
Our framework enables scoring CDAs based on semantic predictability and allows prediction of community membership from minimal textual input. It offers practical benefits, such as providing proxy labels for low-supervision NLP tasks, and is adaptable to other social platforms. Limitations include potential noise in CDA-generated labels but the approach offers a generalizable method for evaluating CDA performance and the coherence of online social groups.
Authors
- Ixandra Achitouv (ORCID: https://orcid.org/0000-0001-9731-9381)
- David Chavalarias (ORCID: https://orcid.org/0000-0001-9485-1399)
- Bruno Gaume
Institutions
- Centre National de la Recherche Scientifique (FR)
- Sorbonne Université (FR)
- Centre d'Analyse et de Mathématique Sociales (FR)
- Laboratoire Cognition, Langues, Langage, Ergonomie (FR)
- Institut de Recherche en Informatique Fondamentale (FR)
- LIP6 (FR)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-11-13
- DOI
- https://doi.org/10.3389/frai.2025.1635436
- Primary Topic
- Complex Network Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00