Fake News Detection
Rajalakshmi B1, Nithin Sebastian2
1Rajalakshmi B, Department Computer Science, St. Albert’s College (Autonomous), Ernakulam, Kerala, India.
2Nithin Sebastian, Department Computer Science, St. Albert’s College (Autonomous), Ernakulam, Kerala, India.
Manuscript received on 25 April 2024 | Revised Manuscript received on 06 May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 13-16 | Volume-4 Issue-1 May 2024 | Retrieval Number: 100.1/ijdm.A163804010524 | DOI: 10.54105/ijdm.A1638.04010524
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The spread of false information on the internet has become a major social issue, casting doubt on the veracity of information shared on these platforms. This study uses cutting-edge methods from machine learning (ML) and natural language processing (NLP) to present a complete framework for the detection of fake news. The purpose of this paper is to develop a model for detecting bogus news. A model is selected by using supervised learning techniques. In addition, we categorize news stories as real or fraudulent using the Naïve Bayes, Logistic Regression, and Random Forest algorithms. Our methodology offers an approach to false news identification that is more robust by taking into account the credibility of the news sources in addition to the content of the news. Using labeled datasets of fictitious and authentic news stories, we train our algorithms. A few methodologies were compared to achieve varying degrees of accuracy. When compared to the other two models, Random Forest is thought to have produced the best results in terms of accuracy. We assess our framework’s effectiveness using real-world news articles and benchmark datasets, showcasing its versatility in correctly recognizing false information in a variety of settings and domains. We demonstrate the advantages of our method in terms of detection accuracy, scalability, and computational efficiency by comprehensive experimentation and comparative analysis. All things considered, our suggested framework is a major step forward in the fight against the dissemination of false information on the internet and provides a workable way to lessen the negative effects of fake news on people, communities, and society at large.
Keywords: Fake News Detection, Fake News, Naïve Bayes, Logistic Regression, Random Forest, Accuracy.
Article of the Scope: Data Science