Abstract
The increasing consumption of news on social media platforms is mainly due to its cheap and attractive nature and it’s capable of spreading the fake news. The spread of fake news has negative effects on society. Some people make it up to get attention or gain political gain. Machine learning and deep learning techniques have been developed to detect fake news. However, they tend to generate inaccurate reports. To detect fake news, we used a Hybrid model that combines SVM and Naive Bayes (NBSVM) framework. It was able to classify the news with an accuracy of 84.85%. This model was tested and trained on a fake news challenge dataset. We used various evaluation metrics (precision, recall, F1- measure, etc.) to measure the model's efficiency
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Emerging Trends in Engineering Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.