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.
As the internet continues to evolve, so do the challenges of managing and moderating online content. This study delves into the use of large language models (LLMs) as a promising tool for identifying public threats posted online. By leveraging automated content analysis techniques, early identification and moderation of threatening rhetoric and advance notices of violence may become more feasible.
The researchers developed custom data collection tools to gather post titles from a popular Korean online community. The dataset consisted of 500 non-threat examples and 20 threats, enabling the evaluation of various LLMs, including GPT-3.5, GPT-4, and PaLM.
What is particularly fascinating about this research is the multi-disciplinary nature of the concepts involved. It combines aspects of natural language processing, machine learning, and data analysis to address real-world societal challenges. By utilizing LLMs, which have been trained on vast amounts of text data, the models can classify individual posts as either “threat” or “safe.”
Importantly, the statistical analysis revealed that all LLMs showcased strong accuracy in identifying threats and non-threats. These models passed chi-square goodness of fit tests, indicating robust performance in both categories. Of the LLMs evaluated, GPT-4 emerged as the top performer with 97.9% accuracy in classifying non-threats and 100% accuracy in detecting threats.
Furthermore, the affordability analysis demonstrated that PaLM API pricing is highly cost-efficient. This is a significant finding as it suggests that implementing LLMs for content moderation at scale is not only effective but also economically feasible.
The implications of these findings are noteworthy for individuals and organizations tasked with content moderation. LLMs have the potential to serve as a valuable tool in augmenting human efforts and mitigating emerging online risks. By automating part of the moderation process, human moderators can focus on higher-level decision-making and interventions.
However, it is crucial to acknowledge the potential biases, lack of transparency, and ethical concerns that surround the deployment of LLMs in content moderation. Biases within the training data could result in unfair or inaccurate classifications. Transparency is essential to understand how these models make decisions, ensuring accountability and trust. Ethical oversight is vital to safeguard against unintended consequences and potential harm to online communities.
Overall, this research highlights the potential of LLMs to address a pressing societal need. It also prompts us to consider the multidisciplinary nature of the concepts involved, encouraging collaborations between experts in natural language processing, machine learning, ethics, and social sciences. By working together, we can harness the power of technology while addressing the ethical and societal implications that arise from its use.