ITM Web Conf.
Volume 54, 20232nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
|Number of page(s)||9|
|Published online||04 July 2023|
- C. Castillo, M. Mendoza, and B. Poblete, “Information credibility on twitter,” in Proceedings of the 20th international conference on World wide web, Mar. 2011, pp. 675–684. doi: 10.1145/1963405.1963500. [CrossRef] [Google Scholar]
- P. S. Reddy, D. Elizabeth Roy, P. Manoj, M. Keerthana, and P. V. Tijare, “A study on fake news detection using naïve bayes, SVM, neural networks and LSTM,” J. Adv. Res. Dyn. Control Syst., vol. 11, no. 6 Special Issue, pp. 942–947, 2019. [CrossRef] [Google Scholar]
- D. Mouratidis, M. N. Nikiforos, and K. L. Kermanidis, “Deep Learning for Fake News Detection in a Pairwise Textual Input Schema,” Computation, vol. 9, no. 2, p. 20, Feb. 2021, doi: 10.3390/computation9020020. [CrossRef] [Google Scholar]
- P. Bahad, P. Saxena, and R. Kamal, “Fake News Detection using Bi-directional LSTM-Recurrent Neural Network,” Procedia Comput. Sci., vol. 165, no. 2019, pp. 74–82, 2019, doi: 10.1016/j.procs.2020.01.072. [CrossRef] [Google Scholar]
- R. K. Kaliyar, A. Goswami, and P. Narang, “FakeBERT: Fake news detection in social media with a BERT-based deep learning approach,” Multimed. Tools Appl., vol. 80, no. 8, pp. 11765–11788, 2021, doi: 10.1007/s11042-020-10183-2. [CrossRef] [Google Scholar]
- T. Pavlov and G. Mirceva, “COVID-19 Fake News Detection by Using BERT and RoBERTa models,” in 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), May 2022, pp. 312–316. doi: 10.23919/MIPRO55190.2022.9803414. [CrossRef] [Google Scholar]
- M. A. Alonso, D. Vilares, C. Gómez-Rodríguez, and J. Vilares, “Sentiment Analysis for Fake News Detection,” Electronics, vol. 10, no. 11, p. 1348, Jun. 2021, doi: 10.3390/electronics10111348. [CrossRef] [Google Scholar]
- O. A. Hanshal, O. N. Ucan, and Y. K. Sanjalawe, “Hybrid deep learning model for automatic fake news detection,” Appl. Nanosci., vol. 13, no. 4, pp. 2957–2967, Apr. 2023, doi: 10.1007/s13204-021-02330-4. [CrossRef] [Google Scholar]
- S. Raza and C. Ding, “Fake news detection based on news content and social contexts: a transformer-based approach,” Int. J. Data Sci. Anal., vol. 13, no. 4, pp. 335–362, 2022, doi: 10.1007/s41060-021-00302-z. [CrossRef] [Google Scholar]
- W. S. Paka, “Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets”. [Google Scholar]
- J. George, S. M. Skariah, and T. A. Xavier, “Role of Contextual Features in Fake News Detection: A Review,” in 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), Feb. 2020, vol. 10, no. 2, pp. 1–6. doi: 10.1109/ICITIIT49094.2020.9071524. [Google Scholar]
- K. Sharma, F. Qian, H. Jiang, N. Ruchansky, M. Zhang, and Y. Liu, “Combating Fake News,” ACM Trans. Intell. Syst. Technol., vol. 10, no. 3, pp. 1–42, May 2019, doi: 10.1145/3305260. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.