International Journal of Software Computing and Testing
A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction
1 Assistant Professor, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats (GIET), Bhubaneswar, Odisha, India
2 Student, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats (GIET), Bhubaneswar, Odisha, India
3 Student, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats (GIET), Bhubaneswar, Odisha, India
Abstract
The broad sharing of information throughout human history was greatly accelerated by the emergence of the World Wide Web and the swift rise of social networking platforms such as Facebook and Twitter. Due to the extensive use of social media, users now generate and distribute vast amounts of content at an unprecedented rate, and a portion of this information is inaccurate or detached from factual reality. Automatically classifying a written article as misinformation or disinformation can be challenging. Even a subject-matter expert must take several aspects into account before determining the authenticity of an article. This study introduces an automated news classification framework based on machine learning techniques. The research examines multiple linguistic features that help differentiate authentic news from misleading or fabricated content. These features are used to train and evaluate several traditional machine learning algorithms as well as deep learning architectures. Among the tested models, the Deep Neural Network demonstrated the best performance, achieving an accuracy rate of 97%.
Keywords
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