Spam Detection

Spam detection aims to automatically identify unwanted digital communications, safeguarding users and systems from malicious content and resource waste. Current research emphasizes improving accuracy and robustness, particularly with limited labeled data, using techniques like data augmentation and transfer learning with models such as BERT, XGBoost, and other large language models. These advancements are crucial for enhancing online security, protecting user privacy, and improving the efficiency of online platforms by mitigating the pervasive impact of spam.