## 2 Related Works  
In this section, we discuss recent models and approaches that have been proposed for FND, highlighting the evolution from traditional ML techniques to more advanced transformer and DL-based methods. The researchers in [9] introduce a Balanced Multimodal Learning with Hierarchical Fusion (BMLHF) approach for multimodal FND (MFND), focusing on image and text. It introduces a multimodal information balancing module that balances various modal information during optimization. In addition, a hierarchical fusion module is designed to explore the correlation within and between modalities, enhancing the optimization process. The evaluation was carried out on Fakeddit, Weibo, and Twitter datasets. Similarly, in [10], [PERSON] et al. proposes a distinct approach for the multimodal modeling approach called GAMED [10]. It focuses on generating distinctive and discriminative features through modal decoupling. The model was trained on Yang and Fakeddit datasets. The researchers in [11] proposed a multilingual transformer-based FND model using various language-independent technologies. It removes the need to convert the input to English for detection. The researchers used a multilingual FND dataset consisting of samples from five different languages. Similarly, the researchers in [12] performed a comparative analysis on seven BERT-based models using a dataset composed of multilingual news from different sources. The researchers in [13] proposed an MFND using a single model that uses features of text, visual, and knowledge graphs. Unlike other models, the proposed model uses a single model to extract features from images and text. The model used a Twitter dataset to evaluate the model. A comparison among 13 models, including the proposed ones, was made. The researchers in [14] performed an extensive analysis of using FastText word embeddings in different ML and DL models, including the transformers. In addition, the use of XAI further strengthens the approach. Three datasets were used to evaluate the model, namely WELFake, FakeNewsPrediction, and FakeNewsNet. [PERSON] et al. proposed 'RPPG-Fake' for early FND by generating propagation paths using reinforcement learning along the integration of propagation topology structure and temporal depth information [15]. Three datasets named Pheme, PoliFact, and GossipCop were used for experimentation. A comparison among ten baseline models was conducted, and the proposed methodology was found to be the best among them. [PERSON] et al. proposed KEHGNN-FD, a Knowledge Graph-Enhanced Graph Neural Network (GNN) enhanced to gain insight from the news, topics, and other entities [16]. The model was trained on four datasets, named LIAR, FakeNewsNet, COVID-19, and PAN2020. The proposed model was compared with seven baseline models, three based on NLP and four based on GNNs, and the proposed model was found to be the best among them. The researchers in [17] proposed a dual-phase methodology to combine text and graphical data (images and video) to identify FN. BERT and CNN were used to analyze text and visual data, respectively. The model was trained using the ISOT dataset and the MediaEval image verification dataset. Various ML algorithms were tested, and RF was found to be the best-performing one. [PERSON] and [PERSON] proposed an ML model to detect FN with the concepts of feature selection (using XGBoost) and cross-validation [18]. The model was trained and evaluated using the LIAR dataset. A comparative analysis was conducted among various ML algorithms and found that the RF worked the best. [PERSON] et al. proposed a progressive fusion model for MFDN [19]. To extract features from different data, a BERT-based feature extractor (text), a visual extractor (Discrete Fourier Transformer (DFT) and VGG19), and a progressive multimodal feature fusion process were used. The model was trained and evaluated on the Weibo and Twitter datasets. The approach was compared with and found to be better than various singular approaches as well as multidomain approaches.