Fake News Detection Using Boosting Ensemble Method

Around the globe, Fake news is a raising concern and has several negative repercussions on people, societies, and countries. It has several detrimental effects on society, including disseminating false information and causing confusion, inciting social unrest and division, eroding credibility and confidence, endangering public health, and causing political instability. To protect people from damage, preserve informational accuracy, stop the spread of hate speech, support a vibrant democracy, and maintain journalism's reputation, fake news detection is crucial. In this work, we attempt to tackle this problem by putting forth a method that uses machine learning classification to distinguish between authentic and fraudulent content. Natural language processing and machine learning ensemble approach are taken into consideration for this. In natural language processing, TF-IDF and Word2vec features are used and optimized using wrapper method (via RFE algorithm). Training and testing are done on “Fake News Detection” dataset from Kaggle. Exploratory examination upon the boosting ensemble method tells that our learners perform better than other options.