Table 22.
False Positive Rate (FPR) and False Negative Rate (FNR)
| Word Embedding Model | Classification Model | FPR | FNR |
|---|---|---|---|
| TF-IDF (using unigrams and bigrams) | Neural Network | 0.04684 | 0.0742 |
| BOW (Bag of words) | Neural Network | 0.1040 | 0.0862 |
| Word2Vec | Neural Network | 0.1320 | 0.3416 |
| GloVe | MNB | 0.1151 | 0.0752 |
| GloVe | DT | 0.3956 | 0.1303 |
| GloVe | RF | 0.3458 | 0.2259 |
| GloVe | KNN | 0.7299 | 0.1931 |
| BERT | MNB | 0.0985 | 0.0789 |
| BERT | DT | 0.1660 | 0.2429 |
| BERT | RF | 0.1245 | 0.3318 |
| BERT | KNN | 0.4037 | 0.4110 |
| GloVe | CNN | 0.0989 | 0.0776 |
| GloVe | LSTM | 0.0080 | 0.0482 |
| BERT | CNN | 0.0590 | 0.0872 |
| BERT | LSTM | 0.0077 | 0.0451 |
| BERT | FakeBERT | 0.0160 | 0.0059 |