A Machine Learning Based Approach for Improved Fake News Detection

Atul Suryawanshi, Vijendra Palash, Priyank Nayak

Abstract


The News is significant piece of our life. In everyday life current news are useful to improve information what occur all throughout the planet. So the vast majority of people groups lean toward watching news a large portion of the people groups for the most part favor perusing paper promptly toward the beginning of the day appreciating with cup of tea. On the off chance that news is phony that will delude people groups now and then phony word used to get out bits of gossip about things or it will influence some political pioneer positions on account of phony news. So it's vital to track down the phony news. This exploration proposed an advanced framework to distinguish counterfeit news, yet now daily's information on web or online media is expanding immensely and it is so rushed to recognize news is phony or not by looking all information and it is tedious so we use characterization strategies to order colossal information. This paper proposed fake news detection system based on the classification approach such as Naïve bayes (NB), Support vector machine (SVM), K Nearest Neighbor (KNN) and Decision Tree (TD)


Keywords


Machine Learning, Fake News, KNN, DT, NB, SVM, Python.

Full Text:

PDF

References


S. Helmstetter and H. Paulheim, "Weakly Supervised Learning for Fake News Detection on Twitter," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018, pp. 274-277, doi: 10.1109/ASONAM.2018.8508520.

] N. Smitha and R. Bharath, "Performance Comparison of Machine Learning Classifiers for Fake News Detection," 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), 2020, pp. 696-700, doi: 10.1109/ICIRCA48905. 2020 .918 3072.

S. I. Manzoor, J. Singla and Nikita, "Fake News Detection Using Machine Learning approaches: A systematic Review," 2019 3rd International Conference on Trends in Electronics and Informatics(ICOEI),2019,pp.230-234,doi: 10.1109/ICOEI.2019. 8862770.

S. Lyu and D. C. -T. Lo, "Fake News Detection by Decision Tree," 2020 SoutheastCon, 2020, pp. 1-2, doi: 10.1109/Southeast Con 44009.2020.9249688.

W. Antoun, F. Baly, R. Achour, A. Hussein and H. Hajj, "State of the Art Models for Fake News Detection Tasks," 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), 2020, pp. 519-524, doi: 10.1109 /ICIoT48696.2020.9 089 487.

S. B. Parikh and P. K. Atrey, "Media-Rich Fake News Detection: A Survey," 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2018, pp. 436-441, doi: 10.1109/MIPR.2018.00093.

M. Kumar Jain, D. Gopalani, Y. Kumar Meena and R. Kumar, "Machine Learning based Fake News Detection using linguistic features and word vector features," 2020 IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), 2020, pp. 1-6, doi: 10.1109/UPCON50219.2020.9376576.

K. Poddar, G. B. Amali D. and K. S. Umadevi, "Comparison of Various Machine Learning Models for Accurate Detection of Fake News," 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), 2019, pp. 1-5, doi: 10.1109/i-PACT44901.2019.8960044.

D. K. Sharma, S. Garg and P. Shrivastava, "Evaluation of Tools and Extension for Fake News Detection," 2021 International Conference on Innovative Practices in Technology and Management (ICIPTM), 2021, pp. 227-232, doi: 10.1109/ICIPTM52218.2021.9388356.

I. Vogel and M. Meghana, "Detecting Fake News Spreaders on Twitter from a Multilingual Perspective," 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020, pp. 599-606, doi: 10.1109/DSAA49011.2020.00084.

V. V. Hirlekar and A. Kumar, "Natural Language Processing based Online Fake News Detection Challenges – A Detailed Review," 2020 5th International Conference on Communication and Electronics Systems (ICCES), 2020, pp. 748-754, doi: 10.1109/ ICCES48766 .2020 .91 37915.

M. Wongkar and A. Angdresey, "Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter," 2019 Fourth International Conference on Informatics and Computing (ICIC), 2019, pp. 1-5, doi: 10.1109/ICIC47613.2019.8985884.

W. Le, M. Lin, L. Jia, J. Ai, X. Fu and Z. Chen, "Multi-Objective Optimization of an Air-Cored Axial Flux Permanent Magnet Synchronous Machine with Segmented PMs based on Support Vector Machine and Genetic Algorithm," 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), 2019, pp. 1-4, doi: 10.1109/ICEMS.2019.8922465.

M. Murugappan et al., "Facial Expression Classification using KNN and Decision Tree Classifiers," 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP),2020,pp.1-6, doi: 10.1109/ICCCSP49186.2020.9315234.




DOI: https://doi.org/10.26483/ijarcs.v12i4.6756

Refbacks

  • There are currently no refbacks.




Copyright (c) 2021 International Journal of Advanced Research in Computer Science