Imagine a world where artificial intelligence can sift through the mountains of unstructured text data online, pinpoint legal violations buried within social media posts and product reviews, and even connect those violations to relevant legal cases. This isn't science fiction—it's the focus of cutting-edge research being done right now. Researchers have developed a system using a lightweight AI model called DeBERTa to tackle this complex challenge. Their approach involves two key components: Named Entity Recognition (NER) and Natural Language Inference (NLI). NER works like a digital detective, scanning text to identify specific entities related to legal violations, such as the law being broken, who committed the violation, and who was affected. Meanwhile, NLI acts as a legal matchmaker, linking identified violations to existing class-action cases. This innovative system was put to the test in a competition called the LegalLens 2024 Shared Task. The results were promising, with the NER component achieving an F1 score of 60.01% in identifying violations and the NLI component reaching an impressive 84.73% in connecting violations to related cases. One of the biggest hurdles in this field is the sheer volume and variety of online text. To overcome this, the researchers used clever techniques like data augmentation and paraphrasing to train their models on a richer, more diverse dataset. While the AI model performed admirably, the researchers acknowledge that larger, more powerful models could potentially push the boundaries even further. The ability to automatically detect and analyze legal violations in online text has huge implications. It could empower consumers, strengthen legal processes, and even help prevent future violations. This research demonstrates the potential of AI to bring transparency and efficiency to the legal world, one social media post at a time.