Funder
National Science Foundation
Publisher
Springer Science and Business Media LLC
Reference69 articles.
1. Aggarwal, A., Maurya, K., & Chaudhary, A. (2020). Comparative study for predicting the severity of cyberbullying across multiple social media platforms. 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 871–877. https://doi.org/10.1109/ICICCS48265.2020.9121046
2. Al-Garadi, M. A., Hussain, M. R., Khan, N., Murtaza, G., Nweke, H. F., Ali, I., Mujtaba, G., Chiroma, H., Khattak, H. A., & Gani, A. (2019). Predicting cyberbullying on social media in the big data era using machine learning algorithms: Review of literature and open challenges. IEEE Access, 7, 70701–70718. https://doi.org/10.1109/ACCESS.2019.2918354
3. Anderson, M. (2018). A majority of teens have experienced some form of cyberbullying [Report]. https://www.pewresearch.org/internet/wp-content/uploads/sites/9/2018/09/PI_2018.09.27_teens-and-cyberbullying_FINAL.pdf
4. Anderson, M., & Jiang, J. (2018). Teens, social media & technology [Report].https://www.pewresearch.org/internet/wp-content/uploads/sites/9/2018/05/PI_2018.05.31_TeensTech_FINAL.pdf
5. Balakrishnan, V., Khan, S., & Arabnia, H. R. (2019). Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Computers & Security, 90, 1–11. https://doi.org/10.1016/j.cose.2019.101710
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献