Troll and Abuser Identification in Social Media for Code-Mixed English, Bangla, and Banglish Text Analysis Using LLMs

Social networking platforms have become our center for daily communication. These networks removed all the barriers that used to exist by providing open platform for sharing thoughts, keeping in touch with friends, and even voicing for what we believe in. Unfortunately, this has also opened the door for trolls and online bullies to run rampant. Along with democratizing communication, these platforms have created a distinctive vocabulary, such as the widespread usage of emojis, which are particularly well-liked by younger audiences. “Banglish” a hybrid language that combines Bengali and English is widely used by Bengalis around the world. It creates serious difficulties for traditional content filtering systems. This study examines how to identify trolls and abusive content in Banglish text using sophisticated large language models (LLMs), such as OpenAI GPT-4, Google Gemini, and Meta LLaMA. Our goal is to use these models to improve the security and reduce the toxicity of social media environments, ensuring a more secure online experience for all users.