Open Access
| Issue |
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
|
|
|---|---|---|
| Article Number | 03005 | |
| Number of page(s) | 12 | |
| Section | Deep Learning | |
| DOI | https://doi.org/10.1051/itmconf/20235603005 | |
| Published online | 09 August 2023 | |
- Andre Correia, Julio C. S. Reis, Fabrıcio Murai, Adriano Veloso, and Fabrıcio Benevenuto “Supervised Learning for Fake News Detection”. IEEE Published by the IEEE Computer Society 1541-1672 2019 [Google Scholar]
- Amey Kasbe and Akshay Jain. “Fake News Detection” IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS) 2018 [Google Scholar]
- Hafsa Dar, Waqas Haider Bangyal, Rukhma Qasim, Najeeb Ur Rehman, Zeeshan Ahmad, Laiqa Rukhsar, Zahra Aman, and Jamil Ahmad. “Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches”. Hindawi journal Computational and Mathematical Methods in Medicine 2021, Article ID 5514220 [Google Scholar]
- Kelly Stahl. California State University Stanislaus “Fake news detection in social media” [Google Scholar]
- Issa Traore, Hadeer, Ahmed and Sherif Saad, “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques” International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. [Google Scholar]
- Anurag Goswami, Rohit Kuamr Kaliyar, Pratik Narang, and Soumendu Sinha. “FNDNet - A deep convolutional neural network for fake news detection”. Cognitive Systems Research journal in June 2020 [Google Scholar]
- Iraklis Varlamis, Jamal Abdul Nasir, Osama Subhani Khan. “Fake news detection: A hybrid CNN-RNN based deep learning approach”. International Journal of Information Management and Data Insights April 2021 [Google Scholar]
- Shu K., Mahudeswaran D., Wang S., Lee D., Chen H., and Liu H., “User-Centric Fake News Detection: A Comparative Study of Machine Learning and Deep Learning Approaches”. Was published in the Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). [Google Scholar]
- Vahid Behzada and Bibek Upadhayay. “Hybrid Deep Learning Model for Fake News Detection in Social Networks”. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) [Google Scholar]
- Syed Ishfaq Manzoor, Nikita Dr. Jimmy Singla. “Fake News Detection Using Machine Learning approaches: A systematic Review”. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) [Google Scholar]
- Sidarth Saran, Uma Sharma, and Shankar M. Patil. “Fake News Detection Using Machine Learning Algorithms”. International Journal of creative research thoughts(IJCRT) 2020 [Google Scholar]
- B. N. Alwasel, Z. Khanam, H. Sirafi, and M. Rashid. “Fake News Detection Using Machine Learning Approaches”. IOP Conference Series: Materials Science and Engineering. [Google Scholar]
- Xiaoming Zhang, Yang Yang, Zhoujun Li, Lei Zheng, Jiawei Zhang, Qingcai Cui, and Philip S. Yu. “TI-CNN: Convolutional Neural Networks for Fake News Detection” [Google Scholar]
- Kadek Sastrawan, I.P.A. Bayupati, and Dewa Made Sri Arsa. “Detection of Fake News Using Deep Learning CNN-RNN Based Methods”. ICT express 2022 [Google Scholar]
- Qamber Abbas, Muhammad Umar Zeshan, and Muhammad Asif. “A CNN-RNN Based Fake News Detection Model Using Deep Learning”. 2022 International Seminar on Computer Science and Engineering Technology (SCSET) [Google Scholar]
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