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.

利用大型语言模型（LLMs）检测在线发布的公共威胁的有效性进行了研究。各种 LLMs 被用于分类帖子为 “威胁” 或 “安全”，结果表明 LLMs 能够有效地增强人类内容审查以帮助缓解新兴的在线风险。