Fake Information Detection Using Social Media Networks

An immediate need exists for autonomous detection software which improves itself to address the current issues of digital misinformation and fake news. Natural language processing joined with machine learning technologies should be implemented by fact- checking operations because false information distributes rapidly across all internet platforms. The proposed system implements a Voting Classifier as its main component for fake news detection by combining Logistic Regression (LR) and Support Vector Machine (SVM) and Random Forest (RF) and Passive- Aggressive Classifier (PAC). TF-IDF with n-grams and word embeddings serve as NLP features that enable the classifier to understand text output through a multi-method implementation process. Parameter testing for the model occurred by running benchmark assessments between the new approach along with other viable approaches. techniques related to their performance accuracy alongside robustness and generalization features. Research results prove that ensemble systems achieve better results in accuracy performance along with improved precision outcomes and a higher recall score whereas reaching higher F1-score points when deployed in fake news detection. The process tackles scalability issues by achieving fast operation and clear interpretations thus enabling successful deployment in practical settings. The detection system will achieve better results when different classifiers work together with natural language processing algorithms per findings from this study. The deployment of ensemble learning models brings benefits to fake news control by enabling users to build trust in online sources according to research studies.