| 1 |
Amjad et al. (2020b)
|
TF-IDF, log-entropy, character n-grams, word n-grams |
AdaBoost |
84% F1 Fake, 94% ROC-AUC scores, lower ROC-AUC of 93%. |
| 2 |
Amjad, Sidorov & Zhila (2020)
|
Word n-gram, char n-grams, and functional n-grams |
AdaBoost Classifier’s |
Accuracy 87%, F1 Fake 91% |
| 3 |
Monti et al. (2019)
|
User profile, User activity, Network and spreading, Content |
Convolutional Neural Network (CNN) |
92.7% ROC AUC |
| 4 |
Humayoun (2022)
|
Word n-gram, character n-gram |
Support Vector Machine, CNN Embeddings |
F1 macro 66%, Accuracy 72% |
| 5 |
Rafique et al. (2022)
|
TF-IDF, BoW, Character N-gram, word N-gram |
NF, LR, SVC, GB, PA, Multinomial NB |
Accuracy 95% |
| 6 |
Amjad et al. (2020a)
|
Character bi-gram, MUCS, BoW, Random |
BERT 4EVER, Logistic Regression |
Accuracy 90% |
| 7 |
Amjad et al. (2022)
|
TF-IDF, count-based BoW, word vector embeddings |
SVM, BERT, RoBERta |
F1-macro 67%, Accuracy 75% |
| 8 |
Kalra et al. (2022)
|
N/A |
Ensemble Learning, ROBERTA, ALBERT, Multilingual Bert, xlm-RoBERTa |
Accuracy 59% |
| 9 |
Salahuddin & Wasim (2022)
|
TF-IDF |
Logistic Regression |
F1 Score 72% |
| 10 |
Akhter et al. (2021)
|
BoW, IG |
SVM, Decision Tree, Naive Bayes |
BA 81.6%, AUC 81.5%, MAE 23.5% |
