Exploring the Accuracy of Machine Learning in Detecting Fake News
Nithya Chenthoorani. P1, Mahalakshmi. K2
1Nithya Chenthoorani P, Student, Department of Computer Science and Engineering, Kalaignar Karunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.
2Mahalaksmi K, Research Supervisor, Department of Computer Science and Engineering, Kalaignar Karunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 27 April 2023 | Revised Manuscript received on 06 May 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 44-50 | Volume-12 Issue-6, May 2023 | Retrieval Number: 100.1/ijitee.F95620512623 | DOI: 10.35940/ijitee.F9562.0512623
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Abstract: Identifying fake news is crucial in the fight against misinformation. To achieve this goal, our project employs SVM and NB algorithms. We also utilise sentiment information from both labelled and unlabeled data to enhance the sentiment classifiers’ understanding of fake news within each trend. With the proliferation ofthe internet, there is a growing volume of dubious and intentionallymisleading content. The quality of fake news can be so high that it can be challenging to differentiate it from authentic news. Thus, the use of deep learning and machine learning methodsfor identifying fake news automatically has become significantly crucial. In our project, we preprocess the text using techniques such as stemming, lemmatisation, and stop word removal to create text representations for our models. Our system’s essential features are based on two observations: first, we aim to classify words, and second, our customers receive a filtered subset of fake news. To categorize phoney news based on the social transmission of false news,we experiment with a simple set of language-independent criteria.
Keywords: SVM, NB, Fake News, Machine Learning, Deep Learning, Stemming, Stop Words, Lemmatization
Scope of the Article: Machine Learning