detecting fake news on social media a hybrid approach with linguistic and knowledge-based analysis
Arjun S,Gokulavasan M C,Sameer Basha Sk
Published in International Journal of Advanced Research in Computer Networking,Wireless and Mobile Communications
ISSN: 2320-7248 Impact Factor:1.8 Volume:5 Issue:3 Year: 23 April,2025 Pages:279-283
Abstract
The rapid development of different social media and content-sharing platforms has been largely exploited to spread misinformation and fake news that make people believing in harmful stories. It allows influencing public opinion, and could cause panic and chaos among population, Thus, fake news detection has become an important research topic, aiming at flagging a specific content as fake or legitimate. Fake news admired from various website are collected and that datasets are trained using Logistic regression, Random Forest Classifiers, Nave bayes, SVM and voting classifiers. Checking the dataset using XGBOOST for validation then a novel hybrid fake news detection system that combines Linguistic features and a novel set of knowledge-based features, social context base called fact-verification features. Finally real and imaginary detected using fact verification method which comprise three types of information namely, reputation of the website where the news also published, coverage opinion of well-known fact-checking websites about the news.
Kewords
Linguistic feature, novel set of knowledge-based features, fact-verification features
Reference
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