Enhancing Fake News Detection with Word Embedding: A Machine Learning and Deep Learning Approach

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Abstract
The widespread dissemination of fake news on social media has necessitated the development of more sophisticated detection methods to maintain information integrity. This research systematically investigates the effectiveness of different word embedding techniques—TF-IDF, Word2Vec, and FastText—when applied to a variety of machine learning (ML) and deep learning (DL) models for fake news detection. Leveraging the TruthSeeker dataset, which includes a diverse set of labeled news articles and social media posts spanning over a decade, we evaluated the performance of classifiers such as Support Vector Machines (SVMs), Multilayer Perceptrons (MLPs), and Convolutional Neural Networks (CNNs). Our analysis demonstrates that SVMs using TF-IDF embeddings and CNNs employing TF-IDF embeddings achieve the highest overall performance in terms of accuracy, precision, recall, and F1 score. These results suggest that TF-IDF, with its capacity to highlight discriminative features in text, enhances the performance of models like SVMs, which are adept at handling sparse data representations. Additionally, CNNs benefit from TF-IDF by effectively capturing localized features and patterns within the textual data. In contrast, while Word2Vec and FastText embeddings capture semantic and syntactic nuances, they introduce complexities that may not always benefit traditional ML models like MLPs or SVMs, which could explain their relatively lower performance in some cases. This study emphasizes the importance of selecting appropriate embedding techniques based on the model architecture to maximize fake news detection performance. Future research should consider integrating contextual embeddings and exploring hybrid model architectures to further enhance detection capabilities. These findings contribute to the ongoing development of advanced computational tools for combating misinformation.

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