## 2 Related Work
### ML Algorithms  
In existing studies, diverse ML algorithms have been applied to resolve the issues and challenges of fake news detection. A fake news detection approach [20] utilized a random forest (RF) classifier by incorporating twenty-three textual features. The authors applied different feature selection techniques to identify the most significant features. They compared results with benchmark techniques like GBM, XGBoost, and the Ada Boost regression model. A comparative analysis of machine learning classifiers presented in [21] includes support vector machine (SVM), naive Bayes (NB), random forest (RF), and logistic regression (LR). The study evaluated the performance of these classifiers on diverse datasets. SVM achieved the highest accuracy on the liar, fake job posting, and fake news datasets.  
Moreover, SVM and RF attained the highest accuracy for the fake job posting dataset, followed by LR and NB. Similarly, another fake news detection model [22] introduced a novel genetic algorithm (GA) and ML classifiers as the fitness function, outperforming traditional methods. Subsequently, ref. [23] focused on the automated classification of news articles into real and fake news by extracting different textual properties using an ML approach. They evaluated the performance of various ML algorithms using benchmark datasets. According to the investigation, RF ensemble-based method outperformed other models and attained 90.40% accuracy for news article classification.  
Kaliyar et al. [24] focused on detecting and classifying fake news distributed on social networks to enhance trust and transparency in social network recommendation systems. The authors discussed various traditional ML models for fake news detection and multiclassification using unlabeled data. The proposed approach improved the accuracy of trust-aware recommender systems through a semi-supervised strategy. LR achieved the highest accuracy in classifying fake news. Rathod et al. [25] proposed a model for detecting fake news in online resources and social media platforms. The authors used Natural Language Processing (NLP) and ML techniques to classify news articles as fake and real news based on source authenticity. Rezaei et al. [26] applied ML algorithms, including passive-aggressive (PA), NB, and SVM, to identify fake news. However, relying solely on simple classification methods may not yield optimal results. Integrating ML techniques with text-based processing techniques has significantly improved fake news detection. Challenges arise due to the limited availability of diverse corpora for training and differentiation purposes. Their approach showed better results through experimental analysis using publicly available datasets, achieving accuracy levels of up to 93%.  
Faust et al. [27] focused on leveraging spreading networks and graph features to construct a model for detecting fake news. Fourteen graph features were extracted and evaluated across 13 ML models. Notably, propensity and centrality emerged as key factors influencing the classification process. The best-performing models achieved better accuracy using modified SVM on the Twitter15 and Twitter16 datasets. Chouliara et al. [28] introduced a robust framework for detecting Thai fake news, encompassing three modules: information retrieval, NLP, and ML. A web-crawler information retrieval technique was employed to collect data from Thai online news websites, and relevant features were extracted using NLP. They compared multiple conventional ML models with ensemble-based ML models, and Adda Boost emerged as the best-performing model. Table 1 presents the performance detail of each ML model for fake news classification.