Abstract
Social media plays a major role in several things in our life. Social media helps all of us to find some important news with low price. It also provides easy access in less time. But sometimes social media gives a chance for the fast-spreading of fake news. So there is a possibility that less quality news with false information is spread through the social media. This shows a negative impact on the number of people. Sometimes it may impact society also. So, detection of fake news has vast importance. Machine learning algorithms play a vital role in fake news detection; Especially NLP (Natural Language Processing) algorithms are very useful for detecting the fake news. In this paper, we employed machine learning classifiers SVM, K-Nearest Neighbors, Decision tree, Random forest. By using these classifiers we successfully build a model to detect fake news from the given dataset. Python language was used for experiments.
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
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Fake News Detection;Indian Journal of Data Mining;2024-05-30
2. Fake News Identification for Web Scrapped Data using Passive Aggressive Classifier;2023 International Conference on Network, Multimedia and Information Technology (NMITCON);2023-09-01
3. Fake News Detection Using ML and DL Approaches;2023 International Conference on Circuit Power and Computing Technologies (ICCPCT);2023-08-10
4. Fake News Detection an Effective Content-Based Approach Using Machine Learning Ensemble Techniques;International Journal of Scientific Research in Science and Technology;2023-02-15
5. A Fake News Classification and Identification Model Based on Machine Learning Approach;Information and Communication Technology for Competitive Strategies (ICTCS 2022);2023