Lifelong Learning of Hate Speech Classification on Social Media
This addresses the need for continuously updating hate speech classifiers in dynamic social media environments, but it is incremental as it builds on existing lifelong learning methods.
The paper tackles the problem of hate speech classification on social media by enabling lifelong learning to adapt to new data without forgetting previous knowledge, achieving better performance than common lifelong learning techniques.
Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. However, the amount of data in social media increases every day, and the hot topics changes rapidly, requiring the classifiers to be able to continuously adapt to new data without forgetting the previously learned knowledge. This ability, referred to as lifelong learning, is crucial for the real-word application of hate speech classifiers in social media. In this work, we propose lifelong learning of hate speech classification on social media. To alleviate catastrophic forgetting, we propose to use Variational Representation Learning (VRL) along with a memory module based on LB-SOINN (Load-Balancing Self-Organizing Incremental Neural Network). Experimentally, we show that combining variational representation learning and the LB-SOINN memory module achieves better performance than the commonly-used lifelong learning techniques.