International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 01 | Jan 2025
p-ISSN: 2395-0072
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Social Media Fake News Detection Using DistilBERT Algorithm And SVM Model Sanskar Khaire, Swarup Mane, Sandeep Milake Dr. Anant Bagade Department of Information Technology, SCTR’s Pune Institute of Computer Technology, Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The rapid growth of social media, the
Its primary motivations often include influencing public opinion, sowing discord, or generating profit through sensationalism and clickbait. The consequences of fake news are far-reaching, with the potential to disrupt societal harmony, influence political outcomes, and compromise public health. For instance, misinformation during elections can manipulate voter behavior, while the dissemination of false information during health crises, such as the COVID-19 pandemic, can hinder effective public health responses. The sheer volume and velocity of fake news propagation pose challenges to traditional methods of verification, which are not only laborintensive but also incapable of scaling to meet the demands of the digital age. These limitations have necessitated the development of automated, technology-driven solutions to address the growing menace of fake news. This research paper focuses on leveraging state-of-the-art advancements in natural language processing (NLP) and machine learning to tackle the problem of fake news detection. Specifically, it explores the combined use of DistilBERT and Support Vector Machine (SVM) as a novel approach to identifying and categorizing fake news. DistilBERT, a lightweight and efficient transformer-based model, has emerged as a powerful tool for text analysis due to its ability to capture nuanced semantic and syntactic relationships within language. Unlike traditional NLP models, DistilBERT excels in understanding contextual word meanings, enabling it to identify subtle differences between credible and deceptive content. Its reduced computational complexity, compared to its predecessor BERT, makes it an optimal choice for real-time applications. The SVM classifier, on the other hand, is a robust machine learning algorithm renowned for its effectiveness in binary classification tasks. By analyzing patterns and relationships within a labeled dataset, SVM can accurately distinguish between real and fake news. Integrating DistilBERT's contextual understanding with SVM's classification capabilities creates a synergistic model that significantly enhances the accuracy and efficiency of fake news detection systems. The proposed system is designed to undergo rigorous training using a diverse dataset comprising labeled articles from various domains, including politics, health, and entertainment. This diversity ensures the model's
spread of fake news has become a pressing issue, leading to misinformation and public distrust. This project focuses on developing an efficient system to detect fake news on social media using a combination of DistilBERT and Support Vector Machine (SVM) models. DistilBERT, a pre-trained transformer model, is employed for its ability to understand the contextual meaning of text, capturing relationships between words and phrases, even in complex sentences. The processed text features extracted by DistilBERT are used as input for the SVM model, which acts as a classifier to distinguish between fake and real news. The system is trained on a dataset containing labeled fake and real news articles to learn patterns that characterize misinformation. By combining the language understanding capabilities of DistilBERT with the robust classification power of SVM, the model achieves high accuracy in identifying fake news.The final model offers a practical solution to combat the widespread issue of misinformation on social media platforms, contributing to a more reliable and trustworthy digital information ecosystem. Key Words: (Fake News Detection, DistilBERT, Support Vector Machine (SVM), Natural Language Processing (NLP), Transformer Models, Hybrid Model, Logistic Regression, Misinformation Detection, Deep Learning for NLP.
1.INTRODUCTION In recent years, the rapid proliferation of social media platforms has fundamentally transformed how information is disseminated, accessed, and consumed. The unparalleled speed and reach of these platforms have democratized information sharing, enabling individuals from all walks of life to express their views and contribute to the global discourse. However, this very democratization has brought about a significant and troubling downside: the unchecked spread of fake news. Fake news, characterized as intentionally false or misleading information presented in the guise of legitimate news, has become a pervasive issue.
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