Research Article
Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining
Table 3
Rumor/nonrumor detection results.
| Model | Class | Pheme | Acc. | Prec. | Rec. | F1 |
| SVM-TS | NR | 0.685 | 0.7580 | 0.7620 | 0.7570 | R | | 0.5530 | 0.5390 | 0.5390 |
| BURvNN | NR | 0.7683 | 0.7828 | 0.7436 | 0.7622 | R | | 0.7562 | 0.7930 | 0.7738 |
| TDRvNN | NR | 0.7043 | 0.6743 | 0.8177 | 0.7342 | R | | 0.7778 | 0.5909 | 0.6575 |
| GAN-GRU | NR | 0.7513 | 0.7561 | 0.7419 | 0.7494 | R | | 0.7474 | 0.7607 | 0.7538 |
| Bi-GCN | NR | 0.8240 | 0.8610 | 0.8720 | 0.8650 | R | | 0.7530 | 0.7340 | 0.7410 |
| AARD | NR | 0.8393 | 0.8259 | 0.8693 | 0.8445 | R | | 0.8652 | 0.8091 | 0.8320 |
| AARD-PARG | NR | 0.8484 | 0.8631 | 0.8290 | 0.8448 | R | | 0.8374 | 0.8677 | 0.8515 |
| GACL | NR | 0.8500 | 0.8710 | 0.9010 | 0.8850 | R | | 0.8010 | 0.7500 | 0.7720 |
| One-level MFF-GCN | NR | 0.8547 | 0.8896 | 0.8910 | 0.8899 | R | | 0.7881 | 0.7864 | 0.7859 |
| Two-level MFF-GCN | NR | 0.8587 | 0.8995 | 0.8845 | 0.8919 | R | | 0.7824 | 0.8078 | 0.7948 |
| Three-level MFF-GCN | NR | 0.8632 | 0.8989 | 0.8932 | 0.8960 | R | | 0.7951 | 0.8043 | 0.7995 |
| One-level MFF-GCN (BERT) | NR | 0.8748 | 0.9147 | 0.8937 | 0.9041 | R | | 0.8024 | 0.8382 | 0.8199 |
| Two-level MFF-GCN (BERT) | NR | 0.8855 | 0.9273 | 0.8968 | 0.9118 | R | | 0.8117 | 0.8635 | 0.8368 |
| Three-level MFF-GCN (BERT) | NR | 0.8973 | 0.9404 | 0.9016 | 0.9206 | R | | 0.8230 | 0.8889 | 0.8547 |
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Note: MFF-GCN (BERT) means pretrained MFF-GCN with BERT. Bold values represent the optimal results for this dataset.
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