DETECTION OF FAKE NEWS WITH NATURAL LANGUAGE PROCESSING SYSTEM

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DETECTION OF FAKE NEWS WITH NATURAL LANGUAGE PROCESSING SYSTEM

ABSTRACT

Information sharing on the internet and numerous social platforms have risen significantly in recent years. Finding the reliability of the facts is difficult. News is being spread without the correct context. The detection of fake news has emerged as a new social phenomenon due to the potential impact it can have. The process of identifying fake news is still in its early stages of research. This study uses natural language processing to identify bogus news (NLP). Three different classifiers—logistic regression, decision trees, and random forests—have been used for the suggested methodology. The labeled dataset was created using publicly available data. There has been using of TF-IDF vectorizer. This paper explores the problem, formulates the task, and All of the NLP solution’s steps have been covered. This report also presents a comparison of the classifiers.

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