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Evaluating FakeAlert: A Machine Learning Model for Real-Time Falsified News Detection and Verification

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Evaluating FakeAlert: A Machine Learning Model for Real-Time Falsified News Detection and Verification


Monika Chaudhary | Pratik Gour | Maheshwari Dhapare | Prof. Usha Kosarkar



Monika Chaudhary | Pratik Gour | Maheshwari Dhapare | Prof. Usha Kosarkar "Evaluating FakeAlert: A Machine Learning Model for Real-Time Falsified News Detection and Verification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Emerging Trends and Innovations in Web-Based Applications and Technologies, January 2025, pp.691-699, URL: https://www.ijtsrd.com/papers/ijtsrd75078.pdf

In an era characterized by the rapid dissemination of information, the proliferation of falsified news poses significant challenges to public discourse and societal trust. This paper presents FakeAlert, a machine learning model designed to detect and verify falsified news in real time. Leveraging advanced natural language processing (NLP) techniques and a robust dataset comprising diverse news sources, FakeAlert employs a multi-faceted approach to identify linguistic patterns, sentiment, and source credibility. The model utilizes classification algorithms such as Support Vector Machines (SVM) and Neural Networks to discern genuine news from misinformation effectively.Despite its innovative framework, FakeAlert faces several limitations, including reliance on high-quality training data, susceptibility to false positives and negatives, and challenges in adapting to evolving misinformation tactics. Additionally, the model's performance is contingent upon continuous updates to address emerging trends in fake news creation. This paper discusses the architecture of FakeAlert, evaluates its performance against existing benchmarks, and highlights areas for future research to enhance its accuracy and reliability. The findings underscore the importance of integrating machine learning solutions in the fight against misinformation while acknowledging the need for ongoing adaptation in response to an ever-changing information landscape.

Falsified News, Fake News Detection, Real-Time Verification, Data Quality, Information Integrity, Sentiment Analysis, Classification Algorithms


IJTSRD75078
Special Issue | Emerging Trends and Innovations in Web-Based Applications and Technologies, January 2025
691-699
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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