Development of an AI Anti-Bullying System Using Large Language Model Key Topic Detection
This work addresses bullying detection and response for social media users and platforms, but it appears incremental as it applies existing LLM methods to a specific domain without claiming major breakthroughs.
The paper tackles the problem of identifying coordinated bullying attacks on social media by developing an AI anti-bullying system that uses a large language model to populate an expert system-based network model, with the result being an analysis of the LLM's efficacy for this task.
This paper presents and evaluates work on the development of an artificial intelligence (AI) anti-bullying system. The system is designed to identify coordinated bullying attacks via social media and other mechanisms, characterize them and propose remediation and response activities to them. In particular, a large language model (LLM) is used to populate an enhanced expert system-based network model of a bullying attack. This facilitates analysis and remediation activity - such as generating report messages to social media companies - determination. The system is described and the efficacy of the LLM for populating the model is analyzed herein.