A Multi-Method Validation Framework for Large-Scale Multilingual Text Analytics
Stefano Blando, Domenica Fioredistella Iezzi
Cite this conference paper
@unpublished{blando2026multimethod,
title={A Multi-Method Validation Framework for Large-Scale Multilingual Text Analytics},
author={Blando, Stefano and Iezzi, Domenica Fioredistella},
year={2026},
note={In review at JADT 2026, Palermo, Italy},
institution={University of Rome Tor Vergata}
}Abstract
To distinguish genuine findings from methodological artifacts, this paper proposes a validation framework based on method-invariant patterns. Analyzing 999,152 multilingual reviews across 18 independent techniques (from classical clustering to Transformers), we demonstrate that substantive content accounts for 95.4% of variance, while methodological choice explains less than 3%. The study confirms that robust patterns transcend specific algorithms and implementations. Furthermore, while BERT achieves peak accuracy (91.3%), classical approaches like SVM offer comparable performance (89.1%) with a 29-fold reduction in computational cost.