Study | Social Media | Feature extraction | Classifier | Accusion | Precision | reccal | F1 score | AUC | Statistical test |
[36] | Twitter/Facebook | linguistic features | RF | 60.54 | 58 | 60.5 | 54.7 | - | - |
[37] | | Word2vec | CNN-LSTM | 94.28 | 96.99 | 92.66 | 94.78 | 95.43 | t-test |
[38] | Twitter/Reddit | GloVe & Sentiments | Averaging | 75.12 | 81.15 | 75.12 | 77.01 | - | - |
[39] | | Word2vec | RF | 87.7 | - | - | 87.7 | - | - |
[40] | Bengali S.M | word embeddings | GRU | 81 | 81 | 81 | 81 | - | - |
[41] | | BERT | BERT | 96.06 | 94.88 | 96.86 | 95.86 | 96.11 | - |
[42] | | word embeddings | WOA-CNN | 93.03 | 90.76 | 92.89 | 91.82 | - | - |
[43] | | TF-IDF/LDA | XGBoost | 87 | 86 | 87 | 87 | - | - |
[44] | | GloVe | CNN | 93.7 | 92.9 | 94.1 | 93.3 | - | - |
[45] | | BoW | Blending | 87.21 | - | - | - | - | - |
[46] | | BoW | Bagging | 98.33 | 90.39 | 96.45 | 92.15 | - | - |
[47] | | TF-IDF | SVM | 79.90 | - | - | - | - | - |
[48] | | Fasttext | BiLSTM + CNN | 99.74 | 99.43 | 99.88 | 99.21 | 99.1 | - |
[49] | | LDA | CBPT | 88.39 | - | - | 86.90 | - | - |
[50] | | BERT | CNN | 0.86 | 0.87 | 0.85 | 86 | - | - |
[51] | | TF-IDF | RF | 95 | 99 | 94 | 96 | - | - |
[52] | Facebook & Youtube | TF-IDF | SVM | 75.15 | 77 | 80 | 78 | - | - |
[53] | | Word2vec | LSTM | 92.89 | 0.88 | 0.60 | 71 | - | - |
This work | | ELMo | STACK | 99.54 | 99.45 | 99.84 | 99.65 | 99.98 | a corrected t-test |