Open Access
Issue
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
Article Number 01164
Number of page(s) 11
DOI https://doi.org/10.1051/matecconf/202439201164
Published online 18 March 2024
  1. Gheewala, Akashdeep Singh, and Vishal Patel. “Machine-Learning-Based Twitter Spam Account Detection and Analysis.” Proceedings of the 2nd International Conference on Data Engineering and Communication Technologies, vol. 32, pp. 33-42, Springer, Singapore, 2021. [Google Scholar]
  2. Kumar, Vikas, and Sunil Kumar. “A Deep Learning Model for Twitter Spam Detection.” Applied Soft Computing, vol. 78, pp. 409-418, 2019. [Google Scholar]
  3. Al-Rubaie, Mohammed, et al. “Detecting Malicious Activity in Twitter Using Deep Learning Techniques.” 4. Applied Soft Computing, vol. 105, pp. 107168-107182, 2022. [Google Scholar]
  4. Gupta, Himanshu, et al. “HybridSpam: A Hybrid Deep Learning Approach for Twitter Spam Detection.” Proceedings of the 2020 2nd International Conference on Advances in Computing, Electronics and Communication (ICACEC), pp. 111-115, IEEE, 2020. [Google Scholar]
  5. Kumar, Rahul, et al. “A Comparative Study on Twitter Spam Detection Using Deep Learning Techniques.” Proceedings of the 2020 3rd International Conference on Innovative Engineering and Management (ICIEM), pp. 879-884, IEEE, 2020. [Google Scholar]
  6. Alshammari, Nayef, et al. “LSTM Based Online Twitter Spam Detection with Dynamic Evolving Features.” 8. arXiv preprint arXiv:2208.12019, 2022. [Google Scholar]
  7. Alayba, Abeer, et al. “A Hybrid Approach for Twitter Spam Detection Using Machine Learning and Deep Learning Techniques.” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 3, pp. 1585-1601, 2022. [Google Scholar]
  8. Wang, Yilin, et al. “Twitter Spam Detection Using Deep Learning with Attention Mechanism.” IEEE Access, vol. 7, pp. 184873-184882, 2019. [Google Scholar]
  9. Li, Junzhou, et al. “Twitter Spam Detection Using Deep Learning Ensemble Methods.” Future Generation Computer Systems, vol. 107, pp. 836-845, 2020. [Google Scholar]
  10. Yang, Zhiyuan, et al. “A Hierarchical Deep Learning Model for Detecting Twitter Spam.” IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 447-459, 2020. [Google Scholar]
  11. Lee, K., Eoff, B., & Caverlee, J. (2011). Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM) (pp. 51-58). [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.