The week's pick
Reseach Article
Fake News Detector: FND
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 10 |
| Year of Publication: 2020 |
| Authors: Pravin P. Kharat, Sanyam Sharma, Sayali Tambe, Deepali Vora |
10.5120/ijca2020920002
|
Pravin P. Kharat, Sanyam Sharma, Sayali Tambe, Deepali Vora . Fake News Detector: FND. International Journal of Computer Applications. 176, 10 ( Apr 2020), 13-17. DOI=10.5120/ijca2020920002
Abstract
The idea focuses on providing the information on whether the news is real or fake and with distinguished information about the content or news headline provided by the user into the developed system. The user is a client or a customer, gets particular news or headline from any source which can be news providing application, news blog, website and social networking site; and upload the news content in the proposed system which is a web-based application. After uploading the news content or headline the user clicks the submit button which is available on the website. Then the content is processed accordingly and the metadata of the content is extracted. There are derived parameters on the basis of which calculation of news authenticity is done. The system also uses the Naive Bayes and Term Frequency-Inverse Document Frequency (TFIDF) algorithm which is used to predict the probability of different classes, based on various parameters or attributes. TFIDF i.e. Term Frequency-Inverse Document Frequency is an algorithm used to transform the text into a meaningful representation of numbers. Based upon the parameters and using the respective algorithm the news authenticity is calculated and the result is uploaded. The final result states whether the news is real or fake news and is developed upon the parameters, metadata and algorithm which simultaneously gives the respective result to the user.
References
- S. Ananth, Dr. K. Radha, Dr. S. Prema, K. Nirajan, “Fake News Detection using Convolution Neural Network in Deep Learning”, IJIRCCE, (2019): 1-4
- Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, Huan Liu, “Fake news detection on social media: A data mining perspective”, ACM Explorations Newsletter 19.1 (2018): 22-36
- Sneha Singhania, Nigel Fernandez, and Shrisha Rao, “3HAN: A Deep Neural Network for Fake News Detection”, International Institute of Information Technology, (2018): 1-5
- Shuo Yang, Kai Shu, Suhang Wang, Renjie Gu, Fan Wu, Huan Liu, “Unsupervised Fake News Detection on Social Media: A Generative Approach”, Department of Computer Science and Engineering, USA, (2019): 1-4
- Supanya, Prabhas, “Detecting Fake News with Machine Learning Method”, Department of Computer Engineering Faculty of Engineering, University Bangkok, (2018): 1-3
- Shaheen Karodia, “Fake News Detection on Twitter Proposal”, University of Cape Town Rondebosch Cape Town, (2017): 1-3.
- Eugenio, Gabriele, Marco L.D., Stefano, and Luca A, “Automated Fake News Detection in Social Networks”, Technical Report UCSC-SOE-17-05 School of Engineering, University of California, (2018): 1-4.
Index Terms
Keywords