Cyberbullying Prevention

We utilize qualitative, quantitative, and computational approaches to counter cyberbullying. We have undertaken focus group interviews in school settings to understand cyberbullying [7], undertaken quantitative analysis to understand how social features relate with cyberbullying [3,9], and built predictive models to automatically detect cyberbullying [1-4, 6, 8-9]. Ours was one of the first efforts to use social features to predict cyberbullying [9]. In follow up work, we have understood how multimodal features are useful for cyberbullying detection [3, 6], how detection from multiple modalities can be probabilistically combined for cyberbullying detection [1, 8], how threads with cyberbullying evolve over time [4], and how cyberbullying detection algorithms can be made fairer [1,2].  

Related Publications

  1. Alasadi, J., Ramanathan, A., Atrey, P. & Singh, V. K. (2020). A Fairness-Aware Fusion Framework for Multimodal Cyberbullying Detection. In Proceedings of the IEEE International Conference on Multimedia Big Data. 
  2. Singh, V., & Hofenbitzer, C. (2019). Fairness across network positions in cyberbullying detection algorithms. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 557-559). IEEE.
  3.  Soni, D., & Singh, V. K. (2018). See No Evil, Hear No Evil: Audio-Visual-Textual Cyberbullying Detection. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 164
  4. Soni, D., & Singh, V. K. (2018). Time Reveals All Wounds: Modeling Temporal Dynamics of Cyberbullying Sessions. In Proceedings of the Eleventh International AAAI Conference on Web and Social Media (pp. 684-687)
  5. Huang, Q., Singh, V. K., & Atrey, P. K. (2018). On cyberbullying incidents and underlying online social relationships. Journal of Computational Social Science, 1(2), 241-260. https://doi.org/10.1007/s42001-018-0026-9.
  6. Singh, V. K., Ghosh, S., & Jose, C. (2017). Toward multimodal cyberbullying detection. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2090-2099). ACM.
  7. Singh, V. K., Radford, M. L., Huang, Q., & Furrer, S. (2017). They basically like destroyed the school one day: On Newer App Features and Cyberbullying in Schools. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 1210-1216). ACM
  8. Singh, V. K., Huang, Q., & Atrey, P. K. (2016). Cyberbullying detection using probabilistic socio-textual information fusion. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 884-887). IEEE Press.
  9. Huang, Q., Singh, V. K., & Atrey, P. K. (2014). Cyber bullying detection using social and textual analysis. In Proceedings of the 3rd International Workshop on Socially-Aware Multimedia (pp. 3-6). ACM

Funding and Support

We gratefully acknowledge the support from the National Science Foundation for this project.
We are also grateful to our collaborators at the Rutgers Tyler Clementi Center and the Graduate School of Applied and Professional Psychology for their continuing support of this project.

Datasets

We are happy to share the cyberbullying labeled dataset with other interested researchers. We would ask you to sign an agreement respecting the privacy of the users in the dataset. Please email Vivek Singh (v.singh@rutgers.edu) to request the dataset.