News Verification Machine Learning Model

Authors: Prof. P.A.Lahare, Prof. S.K.Thakare, Nikita Ekhande, Tejaswini Kadam, Pritam Kandalkar

Country: India

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Abstract: This projects endeavors to create a user-centered fake news detection model through the application of classification algorithms within the realm of machine learning. The central challenge involves constructing a model capable of accurately classifying news articles as genuine or fake based on their content, while also accounting for user perspectives and preferences. This undertaking entails careful algorithm selection, feature extraction, and the development of a decision-making framework that aligns with user concerns. The effectiveness of the model will be evaluated using established performance metrics like accuracy and precision, with a strong emphasis on ensuring the model’s transparency and its integration of specific news categories that hold significance to users. By bridging the gap between technical advancements in machine learning and the diverse needs of users, this research aims to empower individuals to make well-informed determinations about the authenticity of news articles.

Keywords: Fake news prediction, Machine Learning, decision-making framework

Paper Id: 230656

Published On: 2024-05-23

Published In: Volume 12, Issue 3, May-June 2024

Cite This: News Verification Machine Learning Model - Prof. P.A.Lahare, Prof. S.K.Thakare, Nikita Ekhande, Tejaswini Kadam, Pritam Kandalkar - IJIRMPS Volume 12, Issue 3, May-June 2024.

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