A Study on Fake News Detection Techniques using Machine Learning |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-8 |
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| Year of Publication : 2023 | ||
| Authors : Rohan Prasad, Ambar Dutta | ||
| DOI : 10.14445/22312803/IJCTT-V71I8P107 | ||
How to Cite?
Rohan Prasad, Ambar Dutta, "A Study on Fake News Detection Techniques using Machine Learning," International Journal of Computer Trends and Technology, vol. 71, no. 8, pp. 47-51, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I8P107
Abstract
Fake news has been emerging often and in great quantity online in recent years due to the explosive growth of online social networks for political and economic goals. Online social network users can easily become infected by these fake news stories using misleading language, which has already had a significant impact on offline culture. Natural Language Processing cannot be used in isolation to solve the problems associated with fake news detection. Without additional factchecking, even a human would struggle to determine an article's veracity. Promptly detecting fake news is a key objective in enhancing the credibility of information in online social networks. This paper aims to evaluate the performance of various machine learning algorithms for detecting fake news with the help of various performance measures. Gradient boosting and random forest algorithms performed better than decision tree and logistic regression algorithms.
Keywords
Classification, Comparative analysis, Fake news, Machine learning, Performance measures.
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