📚 Benchmarking Research

The following projects have also leveraged different forms of technology to combat cyberbullying. These served as additional inspiration as we aimed to create an open-source, community-driven product.

• Perspective is a free API that uses machine learning to identify "toxic" comments. Perspective returns a percentage that represents the likelihood that someone will perceive the text as toxic. Perspecive requires users to register in order to access the API. It also requires users to have a Google account and Google Cloud project to authenticate API requests. Currently, there is no fee to use it but in the future, increases to QPS may incur a fee (Source).
• AS Tracking by STEER is an AI solution that compares the online psychological test results provided by students with its psychological model to flag which students may need more attention and support. This is a commercial product that aims to sell to various school groups.
• @dhavalpotdar created a project on GitHub to detect cyberbullying in tweets using ML Classification Algorithms. However, this project is not active as the last commit was made 2 years ago.
• Academic researchers from Ghent University, University of Antwerp, and University of Cape Town published a research paper focusing on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying (Van Hee, Cynthia et al. “Automatic detection of cyberbullying in social media text.” PloS one vol. 13,10 e0203794. 8 Oct. 2018, doi:10.1371/journal.pone.0203794).
• An academic researcher from Tampere University published a research paper, proposing a model training scheme that can employ fairness constraints on cyberbullying detection models (O. Gencoglu, "Cyberbullying Detection With Fairness Constraints," in IEEE Internet Computing, vol. 25, no. 1, pp. 20-29, 1 Jan.-Feb. 2021, doi: 10.1109/MIC.2020.3032461).