Synthetic vs. Gold: The Role of LLM Generated Labels and Data in Cyberbullying Detection
This addresses the problem of data scarcity and ethical concerns in cyberbullying detection for vulnerable populations like children, offering a scalable solution, though it is incremental in applying existing LLM methods to a specific domain.
The paper tackled the lack of labeled data for cyberbullying detection, especially for children's language, by using LLMs to generate synthetic data and labels, achieving BERT classifier accuracies of 75.8% with synthetic data and 79.1% with LLM-labeled data compared to 81.5% with authentic data.
Cyberbullying (CB) presents a pressing threat, especially to children, underscoring the urgent need for robust detection systems to ensure online safety. While large-scale datasets on online abuse exist, there remains a significant gap in labeled data that specifically reflects the language and communication styles used by children. The acquisition of such data from vulnerable populations, such as children, is challenging due to ethical, legal and technical barriers. Moreover, the creation of these datasets relies heavily on human annotation, which not only strains resources but also raises significant concerns due to annotators exposure to harmful content. In this paper, we address these challenges by leveraging Large Language Models (LLMs) to generate synthetic data and labels. Our experiments demonstrate that synthetic data enables BERT-based CB classifiers to achieve performance close to that of those trained on fully authentic datasets (75.8% vs. 81.5% accuracy). Additionally, LLMs can effectively label authentic yet unlabeled data, allowing BERT classifiers to attain a comparable performance level (79.1% vs. 81.5% accuracy). These results highlight the potential of LLMs as a scalable, ethical, and cost-effective solution for generating data for CB detection.