Title :
Toward Spotting the Pedophile Telling victim from predator in text chats
Author_Institution :
Iowa State Univ., Ames
Abstract :
This paper presents the results of a pilot study on using automatic text categorization techniques in identifying online sexual predators. We report on our SVM and k-NN models. Our distance weighted k-NN classifier reaches an f-measure of 0.943 on test data distinguishing the child and the victim sides of text chats between sexual predators and volunteers posing as underage victims.
Keywords :
Internet; pattern classification; support vector machines; text analysis; Internet; automatic text categorization technique; online sexual predators; pedophile telling victim spotting; support vector machines; text chats; weighted k-NN classifier model; Application software; Data acquisition; Human computer interaction; Internet; Law enforcement; Support vector machine classification; Support vector machines; Testing; Text categorization; World Wide Web;
Conference_Titel :
Semantic Computing, 2007. ICSC 2007. International Conference on
Conference_Location :
Irvine, CA
Print_ISBN :
978-0-7695-2997-4
DOI :
10.1109/ICSC.2007.32
