Exploring the Accuracy of Machine Learning in Detecting Fake News

Author:

Chenthoorani P NithyaORCID, ,K MahalaksmiORCID,

Abstract

Identifying fake news is crucial in the fight against misinformation. To achieve this goal, our project employs SVM and NB algorithms. We also utilize sentiment information from labeled and unlabeled data to improve the sentiment classifiers’ understanding of fake news in each trend. With the proliferation of the internet, there is a growing volume of dubious and intentional lym is leading 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 methods for identifying fake news automatically has become significantly crucial. In our project, we pre-process the text using techniques such as stemming, lemmatization and stop word removal from creating 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 fake news based on the social transmission of false news, we experiment with a simple set of language-independent criteria.

Publisher

Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP

Subject

Electrical and Electronic Engineering,Mechanics of Materials,Civil and Structural Engineering,General Computer Science

Reference5 articles.

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2. Elshrif Elmurngi and Abdelouahed Gherbi "Detecting Fake Reviews through Sentiment Analysis Using Machine Learning Techniques" International Conference on Data Analysis, June 2020, ISBN: 978-1-61208-603-3.

3. Bhanu Prakash Battula, KVSS Rama Krishna and Tai-hoon Kim "An Efficient Approach for Knowledge Discovery in Decision Trees using Inter Quartile Range Transform" International Journal of Control and Automation, Vol. 8, No. 7 (2020), pp. 325-334, ISSN: 2020-4297 IJCA. [CrossRef]

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5. Kamber, Micheline; Winstone, Lara; Gong, Wan et al. / Generalization and decision tree induction: efficient classification in data mining. Proceedings of the IEEE International Workshop on Research Issues in Data Engineering. editor / P. Scheuermann. IEEE, 1997. pp. 111-120

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