2026-10-14T18:19:48Z https://www.scidb.cn/oai

10.57760/sciencedb.35359 2026-05-08T09:09:10Z

2026-05-08 Experimental Dataset for Fake News Detection with CLIP-Guided Cross-Modal Hierarchical Attention doi:10.57760/sciencedb.35359 en This dataset provides the supporting data for the paper "Fake News Detection with CLIP-Guided Cross-Modal Hierarchical Attention" (Manuscript No. 2025-0712, Data Analysis and Knowledge Discovery). It contains four files:(1) Comparative experiment results (CGCHA.csv): covering the Weibo and PHEME datasets. The Weibo dataset includes results comparing CGCHA with 11 baseline models; the PHEME dataset includes results comparing CGCHA with 9 baseline models, totaling 18 unique baselines across both datasets. Evaluation metrics include Accuracy, Precision, Recall, and F1 for both Fake and Real categories. Eight baselines (EANN and MFAN on Weibo; KDMRD, MKEMN, MIN, CMAC, MFAN, and CAF-ODNN on PHEME) report Accuracy only, with other metrics unavailable in the original literature.(2) Ablation study results (CGCHAablation.csv): also covering both Weibo and PHEME datasets, each containing 12 experimental groups corresponding to the full CGCHA model and 11 ablation variants (-CLIP, -BV, -ATT, -CV, -CB, -GF, -Layer1, -Layer2, -Layer3, Swap-Layer, Para-Fusion), designed to validate the contributions of key components including the CLIP encoder, co-attention mechanism, gating fusion, and the three-layer progressive structure.(3) Per-epoch training logs (weibo_accuracy.txt, twitter_pheme_accuracy.txt): recording the full training process of the CGCHA model on the Weibo (2,160 epochs) and PHEME (1,037 epochs) datasets, including training/test loss, accuracy, precision, recall, F1, and per-class metrics for each epoch. The highest test accuracies are 0.9179 (Weibo) and 0.9087 (PHEME), consistent with the summary results in the comparative CSV. multimodal fake news detection; CLIP; cross-modal attention Lv Haobo PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank