###### Abstract  
The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known \"Fake News\" dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naive bayes (MNB), gaussian naive bayes (GNB), Bernoulli naive Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem. fake news detection; deep features; ensemble-based methods; machine learning; deep learning +
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United States presidential election, the impact of fake news on shaping public opinion was evident [9]. False stories circulated widely on social media platforms, leading to a significant influence on people's perceptions. Examples include claims such as Pope Francis endorsing Donald Trump for president and Hillary Clinton's alleged participation in a child trafficking ring. These fabricated stories not only contributed to political polarization but also misled voters, potentially influencing the election's outcome [10]. Additionally, children's exposure to fake news poses a critical concern, as they are more sensitive to believing fake information than adults [11]. Children are still developing critical thinking skills, so they require assistance distinguishing between reliable and unreliable information. Moreover, fake news can propagate negativity among people when consumed by young users [12]. Recent research has shown that users between 18 and 28 are the most active on social media [13]. Consequently, the early identification of fake news on social media has gained popularity and garnered significant attention. Several fake news detection methods, including conventional machine learning (ML) and deep learning (DL) models, have been developed [14]. In traditional approaches, features from the news must be extracted before they are classified. Conversely, DL models can automatically extract significant features from text or images in the news [15]. Due to the time-consuming process of manual feature extraction, DL approaches are often preferred over traditional approaches [16]. Ensemble methods are widely used to merge multiple models into a composite model that achieves better performance than individual models. The accuracy and diversity of each model significantly impacts the effectiveness of an ensemble model. In general, the utilization of ML and DL models to detect fake news stems from their limitations in comprehending and interpreting the intricacies of human language [17]. The alignment prediction of headline and article body pairs was conducted using the term frequency-inverse document frequency (TF-IDF) and a deep neural network [18].