This document discusses detecting patterns of child grooming behavior on social media. It presents an approach for identifying the stages of online child grooming (trust development, grooming, physical approach) using chat conversation data annotated with grooming stages. Features like n-grams, part-of-speech tags, sentiment, psycholinguistic dimensions and discourse patterns are extracted from sentences and used to build classifiers. Analysis finds discourse and psycholinguistic features are effective for automatic classification of grooming stages. Future work involves adding temporal features and distinguishing between teen and predatory sexual content.