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Does Neural Complexity Improve Health Misinformation Detection? A Leakage-Controlled Cross-Corpus Benchmark — arXiv:2610.04636

Source: arXiv
Original: https://arxiv.org/abs/2610.04636
License (as archived): CC0-1.0
Archived: 2026-10-06T05:28:06.639Z
SHA-256 of the archived text: 90d99ad092cfa27070519ce263c817ebba677ac022a5310c3ac1bbd1e597afa4
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How to cite

Does Neural Complexity Improve Health Misinformation Detection? A Leakage-Controlled Cross-Corpus Benchmark — arXiv:2610.04636 — arXiv, https://arxiv.org/abs/2610.04636 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aecbbc19110c (Metraipolis archive dc_aecbbc19110c, sha256 90d99ad092cfa27070519ce263c817ebba677ac022a5310c3ac1bbd1e597afa4)

BibTeX

@misc{dcaecbbc19110c,
  title = {Does Neural Complexity Improve Health Misinformation Detection? A Leakage-Controlled Cross-Corpus Benchmark — arXiv:2610.04636},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.04636},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aecbbc19110c; sha256 90d99ad092cfa27070519ce263c817ebba677ac022a5310c3ac1bbd1e597afa4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.04636. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Increasing architectural complexity is often assumed to improve health misinformation detection, yet reported gains are difficult to interpret when studies use different corpora, preprocessing pipelines, data splits, and leakage controls. This study provides a controlled cross-corpus benchmark of five compact neural architectures (1D-CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM), a soft-voting neural ensemble, and three classical machine-learning baselines using COVID19-FNIR and CONSTRAINT. Exact-text duplicate controls were applied before modelling; all neural systems used a common preprocessing and optimisation protocol, and neural results were repeated across three random seeds. On COVID19-FNIR, the deep ensemble achieved a mean macro-F1 of 0.9963 and ROC-AUC of 0.9994, while individual neural models ranged from 0.9945 to 0.9957 macro-F1. On CONSTRAINT, the ensemble achieved macro-F1 of 0.9272 and ROC-AUC of 0.9811, whereas a linear SVM achieved macro-F1 of 0.9574 and ROC-AUC of 0.9931. Architecture rankings changed across corpora, and simple sparse linear models remained highly competitive. The findings show that model complexity does not provide a stable performance advantage and that benchmark construction can dominate architecture choice. The study contributes a reproducible, leakage-controlled basis for evidence-driven model selection in health misinformation classification

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.04636 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:20.272Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).