Abstract
People are finding it simpler to find and ingest news as a result of the information’s easy access, quick expansion, and profusion on social media and in traditional news outlets. However, it is becoming increasingly difficult to distinguish between true and false information, which has resulted in the proliferation of fake news. Fake news is a term that refers to comments and journalism that intentionally mislead readers. Additionally, the legitimacy of social media sites, where this news is primarily shared, is at stake. These fake news stories can have significant negative effects on society, so it is becoming increasingly important for researchers to focus on how to identify them. In this research paper, we have compared ensemble learning models for identifying fake news by analyzing a report’s accuracy and determining its veracity. The paper’s objective is to use natural language processing (NLP) and machine learning (ML) algorithms to identify false news based on the content of news stories. The algorithms like decision trees, random forests, AdaBoost, and XGBoost classification are used for the project. A web application has been developed using Python Flask framework to mitigate the challenges associated with identifying false information.
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Singh, D., Khan, A.H., Meena, S. (2023). Fake News Detection Using Ensemble Learning Models. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-99-6553-3_4
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DOI: https://doi.org/10.1007/978-981-99-6553-3_4
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