Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00127
Number of page(s) 10
DOI https://doi.org/10.1051/bioconf/20249700127
Published online 05 April 2024
  • Ghosh, S., Roy, N., & Das, A. (2012). Fake user detection in social media using network analysis and machine learning. In Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 1001–1006). IEEE. [Google Scholar]
  • Wang, H., Lu, Y., Feng, X., & Chen, D. (2014). Detecting spam accounts in online social networks using discriminative features. IEEE Transactions on Knowledge and Data Engineering, 26(10), 2511–2525. [Google Scholar]
  • Zhang, L., & Luo, X. (2015). A novel feature selection method for Twitter spam detection. In 2015 IEEE International Conference on Big Data (Big Data) (pp. 1166–1171). IEEE. [Google Scholar]
  • Al-Natour, S., Awajan, A., & Al-Dwairi, M. (2016). A new machine learning approach for detecting spam tweets. Journal of Information Science, 42(5), 669–679. [Google Scholar]
  • Ibrahim, A.E., Nasef, A., & El-Sofany, H. (2017). Machine learning approach for Twitter spam detection. In 2017 13th International Computer Engineering Conference (ICENCO) (pp. 189–194). IEEE. [Google Scholar]
  • Leng, J., Zhang, L., & Li, M. (2018). A machine learning approach to spammer detection in Twitter. IEEE Access, 6, 56357–56367. [Google Scholar]
  • Moradianzadeh, P., Farahbakhsh, R., & Li, J. (2019). Fake news and fake accounts detection in social media via network analysis and machine learning. Journal of Ambient Intelligence and Humanized Computing, 10(2), 619–632. [Google Scholar]
  • Wang, K., Guo, Y., & Li, D. (2020). A hybrid model for detecting spam bots on Twitter using machine learning and network analysis. IEEE Transactions on Computational Social Systems, 7(1), 168–178. [Google Scholar]
  • F. Li, M. Huang, Y. Yang, and X. Zhu. Learning to identify review spam. Proceedings of the 22nd International Joint Conference on Artificial Intelligence; IJCAI, 2011. [Google Scholar]
  • B. Viswanath, M. Ahmad Bashir, M. Crovella, S. Guah, K.P. Gummadi, B. Krishnamurthy, and A. Mislove. Towards detecting anomalous user behavior in online social networks. In, U.S.ENIX, 2014. [Google Scholar]
  • Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-Garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre, Salvador Lima López, Ivan Flores, Karen O’Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan Banda, Martin Krallinger, and Graciela Gonzalez-Hernandez. 2021. Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 21–32, Mexico City, Mexico. Association for Computational Linguistics. [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.