The field of e-commerce review analysis and spam detection is moving towards leveraging advanced big data analytics, machine learning approaches, and large language models to enhance the authenticity and transparency of online reviews. Researchers are exploring innovative methods to detect and classify spam reviews, as well as to optimize metadata generation for search and recommendation platforms. Noteworthy papers in this area include: Leveraging Big Data Frameworks for Spam Detection in Amazon Reviews, which achieved an accuracy of 90.35% in detecting spam reviews using Logistic Regression. SemanticShield: LLM-Powered Audits Expose Shilling Attacks in Recommender Systems, which proposed a two-stage detection framework that integrates item-side semantics via large language models to detect shilling attacks. End-to-End Aspect-Guided Review Summarization at Scale, which presented a scalable large language model-based system that combines aspect-based sentiment analysis with guided summarization to generate concise and interpretable product review summaries. MetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems, which introduced a multi-agent retrieval-augmented generation framework that learns from implicit search feedback to optimize metadata generation. Detecting LLM-Generated Spam Reviews by Integrating Language Model Embeddings and Graph Neural Network, which proposed a hybrid detection model that integrates text embeddings from a pre-trained language model with a gated graph transformer for spam node classification.