Identifying Sexual Predators by SVM Classification with Lexical and Behavioral Features.
Colin W. Morris, Graeme Hirst · 2012
Abstract We identify sexual predators in a large corpus of web chats using SVM classification with a bag-of-words model over unigrams and bigrams. We find this simple lexical approach to be quite effective with an F1 score of 0.77 over a 0.003 baseline. By also encoding the language used by an author’s partners and some small heuristics, we boost performance to an F1 score of 0.83. We identify the most “predatory ” messages by calculating a score for each message equal to the average of the weights of the n-grams therein, as determined by a linear SVM model. We boost performance with a manually constructed “blacklist”. 1 Introduction and