An Innovative Approach for Fake News Detection using Machine Learning

Hisham, Maya; Hasan, Raza; Hussain, Saqib

doi:10.33317/ssurj.565

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Cited by 3 publications

References 22 publications

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Fake news detection models using the largest social media ground-truth dataset (TruthSeeker)

Khalil,

Azzeh

2024

Int J Speech Technol

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Fake news detection models using the largest social media ground-truth dataset (TruthSeeker)

Khalil,

Azzeh

2024

Int J Speech Technol

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Tackling Disinformation: Machine Learning Solutions for Fake News Detection

Sangi,

Nagaram,

Sudulagunta

et al. 2023

Proceedings of the 2023 13th International Conference on Communication and Network Security

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

Al-Tarawneh,

Al-irr,

Al-Maaitah

et al. 2024

Computers

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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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An Innovative Approach for Fake News Detection using Machine Learning

Cited by 3 publications

References 22 publications

Fake news detection models using the largest social media ground-truth dataset (TruthSeeker)

Fake news detection models using the largest social media ground-truth dataset (TruthSeeker)

Tackling Disinformation: Machine Learning Solutions for Fake News Detection

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

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