#Fake copyright claim finetunes verification#
In this paper, we present a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evidential sentences from Wikipedia documents. The increasing concern with misinformation has stimulated research efforts on automatic fact checking. Conclusion: the presented research proves that machine learning is a promising approach to fake news detection. Promising results (accuracy = 0.95, precision = 0.99, recall = 0.91, and F1-score = 0.95) were reported. Realistic, publicly available data was used in order to train and test the classifiers, Results: in the article, several experiments were presented they differ in the implemented classifiers, and some improved parameters. In the proposed method, two classifiers cooperate consequently, they obtain better results. Methods: in this research, a fake news detection method based on multi classifiers (CNN, XGBoost, Random Forest, Naive Bayes, SVM) has been developed. The problem of online disinformation has recently become one of the most challenging issues of computer science. This paper presents how ML can be used for detecting fake news. Background: the machine learning (ML) techniques have been implemented in numerous applications and domains, including health-care, security, entertainment, and sports.
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