HarmLLaMA: Harmful Language Detection with Large Language Models

Online platforms are complex systems that influence the commercial, social, and political environment, debating important real-life topics, e.g., health, emigration, elections, climate change, etc. These online environments offer users freedom of expression through anonymous posting. In addition to their obvious advantages, some users abuse this freedom to spread harmful content, e.g., misinformation, propaganda, harmful conspiracy theories, or abusive, aggressive, and offensive speech. Automated detection techniques can effectively reduce the negative influence of antisocial behavior used by these malicious actors. In this article, we propose HarmLLAMA, a fine-tuned LLAMA2 model using LORA. The experimental results on two real-world datasets show that our model, HarmLLaMA, outperforms current state-of-the-art models in terms of Accuracy, Precision, Recall, and F1-Score.