NLP and Deep Learning for Fake News Classification

• Published in: 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) Vol. 1; pp. 1 - 5
• Main Authors: Vimal, S.P, Abishek, V, Akash, S, Deepak Kumar, B
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27-10-2023
• Subjects: Classification algorithms; Deep learning; Google colab; Machine learning algorithms; Natural language processing; Python; Social networking (online); Support vector machines; Vectors
• DOI: 10.1109/ICCAMS60113.2023.10526083

The prevalence of social media and online news sources, which enable the quick dissemination of false information, has contributed significantly to the problem of fake news in recent years. To solve this issue, natural language processing (NLP) and deep learning techniques have been deployed to automatically identify and classify fake news articles. In this study, we applied four popular machine learning algorithms, namely Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression, to classify fake news articles. The results showed that all four algorithms were capable of achieving high accuracy in classifying fake news articles, with Random Forest performing the best with an accuracy of 96.7%. To make the classification process more accessible to users, we developed a web interface using HTML, CSS, and Flask. The interface allows users to input news articles and receive a classification of whether the article is real or fake. The Flask server uses the machine learning models to classify the article and returns the result to the web application, which displays the classification result on the page using HTML and CSS. Furthermore, a comparative analysis of the four algorithms was conducted to determine which algorithm performed the best in terms of accuracy and computational efficiency. The results indicated that Random Forest outperformed the other algorithms in both accuracy and computational efficiency, Overall, the combination of NLP techniques, machine learning algorithms, and web technologies can provide a powerful tool for combating fake news and promoting more accurate information dissemination.