Efficacy of Utilizing Large Language Models to Detect Public Threat
  Posted Online
Taeksoo Kwon Connor Kim
This paper examines the efficacy of utilizing large language models (LLMs) to
detect public threats posted online. Amid rising concerns over the spread of
threatening rhetoric and advance notices of violence, automated content
analysis techniques may aid in early identification and moderation. Custom data
collection tools were developed to amass post titles from a popular Korean
online community, comprising 500 non-threat examples and 20 threats. Various
LLMs (GPT-3.5, GPT-4, PaLM) were prompted to classify individual posts as
either "threat" or "safe." Statistical analysis found all models demonstrated
strong accuracy, passing chi-square goodness of fit tests for both threat and
non-threat identification. GPT-4 performed best overall with 97.9% non-threat
and 100% threat accuracy. Affordability analysis also showed PaLM API pricing
as highly cost-efficient. The findings indicate LLMs can effectively augment
human content moderation at scale to help mitigate emerging online risks.
However, biases, transparency, and ethical oversight remain vital
considerations before real-world implementation.