Open Access
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
Article Number 03017
Number of page(s) 5
Section Computing
Published online 05 May 2022
  1. Mykhailo Granik, Volodymyr Mesyura, “Fake News Detection Using Naive Bayes Classifier”, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON). [Google Scholar]
  2. H. Gupta, M. S. Jamal and M. S. Desarkar, “A framework for real-time spam detection in Twitter,” 2018 10th International Conference on Communication Systems Networks (COMSNETS). Bengaluru, 2018, pp. 380–383 [CrossRef] [Google Scholar]
  3. Marco L. Della Vedova, Eugenio Tacchini, Gabriele Ballarin, Luca de Alfaro, “Automatic Online Fake News Detection Combining Content and Social Signals”, ISSN 2305-7254, 2017 [Google Scholar]
  4. Cody Buntain, Jennifer Golbeck “Automatically Identifying Fake News in Popu- larTwitter Threads”, 2017 IEEE International Conference on Smart Cloud. [Google Scholar]
  5. Shloka Gilda, “Evaluating Machine Learning Algorithms for Fake News Detection” 2017 IEEE 15th Student Conference on Research and Development (SCOReD) [Google Scholar]
  6. Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu, “Fake News Detection on Social Mcdia:A Data Mining Perspective” arXiv:1708.01967v3 [cs.SI], 3 Sep 2017 [Google Scholar]
  7. Kushal Agarwalla, Shubham Nandan, Varun Anil Nair, D. Deva Hema, “Fake News Detection using Machine Learning and Natural Language Processing,” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, lssue-6, March 2019 [Google Scholar]
  8. Wang, E., Hussels, P. and Liu, P. (2009) ‘Securely and flexibly sharing a biomedical data management system’, SPIE. [Google Scholar]
  9. Rada Mihalcea, Carlo Strapparava, The lie detector: explorations in the automatic recognition of deceptive language. Proceedings of the ACL-IJCNLP [Google Scholar]
  10. Deligiannis, N.; Huu, T.D.; Nguyen, D.M.; and Luo, X. 2018. Deep Learning for Geolocating Social Media Users and Detecting Fake News. [Google Scholar]
  11. Gross, N., and Simmons, S. 2006. Americans’ views of political bias in the academy and academic freedom. In annual meeting of the American Association of University Professors. [Google Scholar]
  12. Ma, J.; Saul, L.K.; Savage, S.; and Voelker, G.M. 2009. Beyond blacklists: learning to detect malicious web sites from suspicious urls. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 1245–1254. ACM. [CrossRef] [Google Scholar]
  13. Long, Y.; Lu, Q.; Xiang, R.; Li, M.; and Huang, C.-R. 2017. Fake News Detection Through MultiPerspective Speaker Profiles, hi Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), 252–256. Taipei, Taiwan: Asian Federation of Natural Language Processing. [Google Scholar]
  14. Main, T. J. 2018. The Rise of the Alt-Right. Brookings Institution Press. [Google Scholar]
  15. Katsaros, D.; Stavropoulos, G.; and Papakostas, D. 2019. Which machine learning paradigm for fake news detection? In 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 383–387 [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.