Leveraging cross-platform data to improve automated hate speech detection
This work is significant for social media platforms and researchers by providing a more robust and generalizable method for automated hate speech detection, which is often limited by platform-specific language nuances.
This paper addresses the challenge of detecting hate speech across different social media platforms. The authors propose a superlearner model that combines datasets and classification models from multiple platforms, demonstrating improved detection performance and applicability to novel platforms not included in the training data.
Hate speech is increasingly prevalent online, and its negative outcomes include increased prejudice, extremism, and even offline hate crime. Automatic detection of online hate speech can help us to better understand these impacts. However, while the field has recently progressed through advances in natural language processing, challenges still remain. In particular, most existing approaches for hate speech detection focus on a single social media platform in isolation. This limits both the use of these models and their validity, as the nature of language varies from platform to platform. Here we propose a new cross-platform approach to detect hate speech which leverages multiple datasets and classification models from different platforms and trains a superlearner that can combine existing and novel training data to improve detection and increase model applicability. We demonstrate how this approach outperforms existing models, and achieves good performance when tested on messages from novel social media platforms not included in the original training data.