Systems and methods for unsupervised cyberbullying detection via time-informed Gaussian mixture model
US11916866B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 9, 2021 |
| Grant date | Feb 27, 2024 |
| Priority date | — |
| Expiry date | Dec 27, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04W12/122
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
A computer-implemented framework and/or system for cyberbullying detection is disclosed. The system includes two main components: (1) A representation learning network that encodes the social media session by exploiting multi-modal features, e.g., text, network, and time; and (2) a multi-task learning network that simultaneously fits the comment inter-arrival times and estimates the bullying likelihood based on a Gaussian Mixture Model. The system jointly optimizes the parameters of both components to overcome the shortcomings of decoupled training. The system includes an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared to supervised models.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.