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Title DETECTION OF FAKE NEWS USING NATURAL LANGUAGE PROCESSING AND STATISTICAL TECHNIQUES

Author(s) PARCHAMDAR ABBAS

Abstract Detection of Fake News using Natural Language Processing and Statistical Techniques The rapid growth of fake news in online media and social platforms has become a major concern in recent years. To address this problem, we propose a system that integrates Statistical techniques, Machine Learning (ML), and Natural Language Processing (NLP) methods to identify fake news. We extract features using the Term Frequency- Inverse Document Frequency (TF-IDF) method and evaluate the classifier’s performance using metrics such as accuracy and precision. We have used three different ML algorithms for classification, namely Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR). Our results show that LR with Maximum Likelihood Estimation (MLE) outperforms other classifiers, achieving an accuracy of 95%. Our study includes the use of NLP methods for feature extraction, which improves the accuracy of fake news detection. In addition, we demonstrate that LR with MLE is an effective approach for identifying fake news by reducing the complexity and dimension of features, which can help to prevent the spread of fake news. The study provides a valuable contribution to the field of fake news detection and highlights the importance of integrating NLP methods with Statistical and ML techniques to improve the effectiveness of fake news detection.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2023-06-02

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