## 2 Related Work
### DL Algorithms  
ML models rely on predefined features and statistical algorithms to detect fake news. In contrast, DL models automatically learn features and hierarchies from raw data, enabling these models to capture more intricate patterns and representations. Ahmed et al. (2019) proposed a novel approach for detecting fake news related to COVID-19 using biomedical information extraction (BioIE) combined with ML models. The authors analyzed COVID-19 news articles, extracted novel features using BioIE algorithms, and trained ML classifiers. The study demonstrated that incorporating biomedical information improves the performance of fake news detection models. Rai et al. (2019) proposed techniques that rely on natural language analysis and DL models. They applied a hybrid approach to combine CNN with an attention-based mechanism to highlight the significant part of textual data. The utilization of attention in CNN models enables them to concentrate on specific segments of input data during prediction and output generation. This process assigns different levels of significance to individual elements within a sequence, facilitating the model in grasping pertinent context for enhanced comprehension and generation capabilities (Rai et al., 2019). However, CNN-based models cannot learn past and future dependencies from a text but have become popular due to their convergence to near-optimal solutions with low computational complexity. Luvenbe et al. (2019) proposed a multimodal transformer using two-level visual features (MTTV), which leverages transformer models to process text and image data uniformly. Two-level visual features, global and entity levels, are utilized to enhance the utilization of news images. The extended transformer model enables full interaction between multimodal data, capturing semantic relationships. The authors also introduced a scalable classifier to improve the balance of fine-grained fake news detection. Experimental results demonstrated the effectiveness of MTTV compared to existing methods. Prabhakar Kaila et al. (2019) proposed a dual-channel CNN (DC-CNN) model with attention-pooling to address challenges such as noisy data, redundancy, and long-distance dependencies in fake news detection. DC-CNN utilizes skip-gram and fast-Text deep embeddings to reduce noisy data and enhance the model's learning ability for non-derived words. A parallel dual-channel pooling layer replaces the traditional CNN pooling layer, combining the advantages of max-pooling for local information and attention-pooling for context semantics and global dependencies. Experimental results on COVID-19 fake news datasets showed that the proposed model performed best in handling noisy data and balancing local-global feature correlations. Onan et al. (2019) introduced a method for assessing the credibility of knowledge-based facts using a multi-layer perceptron (MLP) model to address the limitations of existing models in classifying facts as fake or genuine. Their approach utilizes state-of-the-art word embedding models for feature embedding, such as Word2 Vec, GloVe, and TF-IDF. The experimental results showed that the MLP trained on triples vectorized with GloVe and counted vectorize outperformed the baseline ML models in terms of accuracy. The authors also proposed an algorithm for the joint \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Year** & **Model** & **Performance Evaluation Matrix (\%)** \\ \hline
[20] & 2020 & RF + XG Boost & F1 score: 92.00 \\ \hline
[21] & 2021 & SVM & Accuracy: 91.70 \\ \hline
[22] & 2022 & GA & Accuracy: 80.50 \\ \hline
[23] & 2022 & RF & Accuracy: 90.40 \\ \hline
[24] & 2020 & Semi-Supervised LR & Accuracy: 87.91 \\ \hline
[25] & 2022 & Random subspace (SVM) & Accuracy: 89.76 \\ \hline
[26] & 2022 & PA + NB & Accuracy 93.00 \\ \hline
[27] & 2023 & Bagging + SVM & Accuracy: 91.40 \\ \hline
[28] & 2023 & Boosting + Add Boost & Accuracy: 94.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ML algorithms for classifying fake news.