International Research Journal of Education and Technology

In the current digital era, fake news is a widespread problem that poses serious threats to the credibility of public debate and the distribution of information. The quick dissemination of false information and misleading content on social media and other platforms threatens public confidence in conventional media sources and shape’s public opinion and decision-making. To create automated systems that can successfully identify and counteract false news, academics and practitioners have resorted to natural language processing (NLP) algorithms in response to this expanding danger. This work explores the state-of-the-art approaches, difficulties, and tactics in the field of false news detection systems through a thorough examination and analysis of systems utilizing natural language processing (NLP) algorithms. The first section of the article gives a general review of the fake news problem and discusses how it has affected politics, society, and public debate. The article goes on to discuss how natural language processing (NLP) algorithms may be used to combat the issue of fake news. These algorithms can be used to evaluate textual data, extract relevant aspects, and categorize news stories as authentic or fraudulent. The study examines several strategies and tactics used in supervised learning, unsupervised learning, and semi-supervised learning techniques used in false news identification utilizing natural language processing algorithms. The preparation procedures for cleaning and formatting textual data are covered, along with feature extraction techniques for capturing linguistic clues and semantic links and classification models for differentiating between phony and real news stories. The study also examines the difficulties and constraints involved in detecting fake news using natural language processing (NLP) algorithms, such as the profusion of false information sources, the dynamic character of fake news, and the existence of evasion techniques and adversarial assaults The report ends by describing potential avenues for further research and advancements in the use of NLP algorithms for false news identification. In addition to highlighting the potential effects of these systems on fostering accountability, credibility, and trust in the digital information ecosystem, it underscores the significance of interdisciplinary collaboration, ethical considerations, and transparency in the development and implementation of fake news detection systems. Overall, this work offers insightful information about the most recent approaches and strategies for identifying fake news using natural language processing (NLP) algorithms, providing a thorough grasp of the difficulties, prospects, and ramifications associated with battling disinformation in the digital era. Key Words: NLP, Machine learning, SVM classifier, Decision Tree, Logistic Regression, Random Forest Classifier, Gaussian Naïve Bayes classifier.