Machine Learning Framework for Detecting Fake News and Combating Misinformation Spread on Facebook Platforms

Authors

  • Poondy Rajan Y Dean administration , Loyola Institute of Business Administration, Chennai , TamilNadu , India – 600034. https://orcid.org/0009-0001-5223-3696
  • Kishore Kunal Professor of Business Analytics, Loyola Institute of Business Administration, Chennai , TamilNadu , India – 600034 https://orcid.org/0000-0003-4154-690X
  • Anitha Palanisamy Associate Professor ,Department of Electronics and Communication Engineering, Sri Sai Ranganathan Engineering College Coimbatore, Tamil Nadu, India. https://orcid.org/0009-0004-8562-7183
  • Senthil Kumar Rajendran Assistant Professor, School Of Management Studies , Vels Institute of Science, Technology & Advanced Studies (VISTAS), Pallavaram, Chennai -117 https://orcid.org/0009-0006-7408-1861
  • Rupesh Gupta Professor, Department of Computer science,Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India https://orcid.org/0000-0001-8229-584X
  • Vairavel Madeshwaren Dhanalakshmi Srinivasan College of Engineering , Coimbatore ,Tamil Nadu ,India -641105

DOI:

https://doi.org/10.22399/ijcesen.1492

Keywords:

Fake News Detection,, Machine Learning,, Facebook, Text Preprocessing, Gradient Boosting,, Performance Metrics

Abstract

The spread of fake news on social media platforms like Facebook threatens societal harmony and undermines the reliability of information. To address this issue, this research employs machine learning techniques to construct a robust and scalable framework for detecting fake news. Using a well-curated dataset of labeled Facebook posts containing both authentic and fake news, the study ensures a balanced representation for effective learning. Textual data was transformed into numerical features through Term Frequency-Inverse Document Frequency (TF-IDF) preprocessing, enabling seamless integration with machine learning algorithms. A variety of classification models, including Support Vector Machines (SVM), Logistic Regression, Gradient Boosting, and Random Forest, were trained and evaluated. Six performance evaluations precision, accuracy, F1 score, recall, Matthews Correlation Coefficient (MCC), and area under the Receiver Operating Characteristic (ROC) curve—were used to measure model effectiveness. The results highlighted Gradient Boosting as the most effective algorithm, achieving superior accuracy and overall performance. This framework demonstrates the capability of machine learning to automate the detection of misinformation, offering a scalable and efficient solution for preserving content credibility on Facebook. The study contributes significantly to the broader effort of combating misinformation, ensuring the dissemination of reliable information, and safeguarding public trust on social media platforms

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Published

2025-04-13

How to Cite

Poondy Rajan Y, Kishore Kunal, Anitha Palanisamy, Senthil Kumar Rajendran, Rupesh Gupta, & Madeshwaren, V. (2025). Machine Learning Framework for Detecting Fake News and Combating Misinformation Spread on Facebook Platforms. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1492

Issue

Vol. 11 No. 2 (2025): IJCESEN

Section

Research Article

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