Abstract
The classification of any information as true or false has piqued the curiosity of researchers all around the world. Different types of studies are done to document the impact of misleading and fake news on the general public, as well as people’s reactions to such news. Falsified news or fabricated posts are any textual or visual content that is fake/false that is created in order for readers to believe in anything that isn’t true. For instance, a news item headlined “Beasts in White Aprons” was recently circulated on the microblogging platform-Facebook, by an acknowledged reporter from Srinagar, J &K, and many began to believe it, despite the fact that it was completely false. Therefore, the main goal of this research is to apply various machine learning models to distinguish between real and fraudulent news. By using several machine learning models to discriminate between authentic and false news, we add to the expanding body of research on identifying fake news in this work. Our model performs better in scenarios in which there is limited data.
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Jain, J. et al. (2024). Fake News Investigation Using Ensemble Machine Learning Techniques. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2026. Springer, Cham. https://doi.org/10.1007/978-3-031-53082-1_8
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DOI: https://doi.org/10.1007/978-3-031-53082-1_8
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