Fake News Detection System Using MultinomialNB and Django Framework

Year : 2025 | Volume : 15 | Issue : 01 | Page : 23 32
    By

    Geeta Makhija,

  • Bhakti Gotarne,

  • Rutuja Mane,

  • Bhakti More,

  1. Assistant Professor, Department of Computer Engineering, Parvatibai Genaba Moze College of Engineering, Wagholi, Pune, Maharashtra, India
  2. Student, Department of Computer Engineering, Parvatibai Genaba Moze College of Engineering, Wagholi, Pune, Maharashtra, India
  3. Student, Department ofDepartment of Computer Engineering, Parvatibai Genaba Moze College of Engineering, Wagholi, Pune Computer Engineering, Parvatibai Genaba Moze College of Engineering, Wagholi, Pune, Maharashtra, India
  4. Student, Department of Computer Engineering, Parvatibai Genaba Moze College of Engineering, Wagholi, Pune, Maharashtra, India

Abstract

The emergence of the World Wide Web and the rapid growth of online platforms have transformed the landscape of news dissemination. However, the rise of social media has also led to an overwhelming influx of potentially unreliable information, making it increasingly challenging to verify the truthfulness of articles. This verification process has become a daunting task, necessitating a thorough examination of various domain-specific aspects to ascertain the credibility of news content. In response to this issue, machine learning algorithms have shown considerable promise in automatically detecting fake news. Researchers are actively employing a diverse range of performance metrics to evaluate the efficacy of these algorithms. Natural language processing (NLP) techniques are essential in this process, as they aid in data pre-processing, thereby improving the accuracy of machine learning models. By leveraging extracted textual properties, researchers can train and evaluate machine learning classifiers designed to distinguish between genuine and fabricated content. This includes leveraging various features and performance metrics to evaluate the effectiveness of these classifiers. Ultimately, this study aims to contribute to the ongoing efforts to combat misinformation in the digital age by providing insights into the intersection of machine learning, NLP, and news verification.

Keywords: Machine learning, fake news, MultinomialNB, social media, term frequency-inverse document frequency (TF-IDF)

[This article belongs to Current Trends in Information Technology ]

How to cite this article:
Geeta Makhija, Bhakti Gotarne, Rutuja Mane, Bhakti More. Fake News Detection System Using MultinomialNB and Django Framework. Current Trends in Information Technology. 2024; 15(01):23-32.
How to cite this URL:
Geeta Makhija, Bhakti Gotarne, Rutuja Mane, Bhakti More. Fake News Detection System Using MultinomialNB and Django Framework. Current Trends in Information Technology. 2024; 15(01):23-32. Available from: https://journals.stmjournals.com/ctit/article=2024/view=191757


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Regular Issue Subscription Review Article
Volume 15
Issue 01
Received 25/09/2024
Accepted 23/12/2024
Published 31/12/2024
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