In the digital age, the rapid spread of misinformation through online platforms has become a significant societal concern. Fake news, often crafted to mislead or manipulate public opinion, can have serious political, economic, and social implications. This research presents a Natural Language Processing (NLP)-based approach for detecting fake news by analyzing textual content from news articles. Using Python and machine learning libraries, a classification model is trained on publicly available datasets to distinguish between real and fake news. The model employs key NLP techniques such as tokenization, TF-IDF vectorization, and supervised learning algorithms including Logistic Regression, Naive Bayes, and Support Vector Machines. Evaluation metrics such as accuracy, precision, recall, and F1-score are used to assess performance. The results demonstrate that NLP combined with machine learning provides an effective and scalable solution for automated fake news detection. This system can be integrated into content moderation tools, social media platforms, or educational settings to promote information integrity.
Fake News Detection, Natural Language Processing, Machine Learning, Text Classification, Misinformation, TF-IDF, Logistic Regression, Python, News Dataset, Information Integrity