ChatCoder: Toward the Tracking and Categorization of Internet Predators

1. ChatCoder: Toward the Tracking and Categorization of Internet Predators April Kontostathis Lynne Edwards Amanda Leatherman Ursinus College

2. Where are we coming from? • Spring/Summer 2008 • Amanda Leatherman, Ursinus class of 2009, approaches Lynne Edwards, Associate Professor of Media and Communication Studies, about a new project.

3. Summer 2009 • Amanda and Lynne research related work • Olson, L. N., Daggs, J. L., Ellevold, B . L., & Rogers, T. K. (2007). The communication of deviance: Toward a theory of child sexual predators' luring communication. Communication Theory, 17, 231-251. • Lynne and Amanda channel this project in two directions • Modify the theory for the online environment • Operationalize the theory

4. Original LCT Model (Olson, et. al) • Gaining Access • Characteristics of the perpetrator • Characteristics of the victim • Strategic placement • Deceptive Trust Development • Grooming • Communicative desensitization • Reframing • Isolation • Approach

5. Process • Read many transcripts from Perverted-justice.com • … not an appealing job

6. Meanwhile … • I am planning a Fall 2008 Software Engineering course – looking for projects to assign to students • Lynne asks if my students can build a system to find phrases in the perverted-justice transcripts • … a collaboration is born!

7. Where are we now? Revised LCT Model • Gaining Access • Strategic Placement • Deceptive Trust Development • Activities • Compliments • Personal Information Exchange • Relationship Exchange • Grooming • Communicative Desensitization • Reframing • Isolation • Approach

8. Categorization Experiments • First Experiment • Class: {Predator , Victim} • 32 instances, 16 in each class (talking to each other) • Eight numeric attributes - Count of tagged phrases in each category • Activities • Personal Information • Compliments • Relationship • Reframing • Desensitization • Isolation • Approach

9. Results • Classifier: C4.5 (J48 in Weka) • 3-fold cross validation • Success Rate: 59% • baseline 50% • Confusion matrix

10. Decision Tree DesensitizationCount <= 35 | RelationshipCount <= 0 | | ActivitiesCount <= 1 | | | IsolationCount <= 5: Predator (5.0/1.0) | | | IsolationCount > 5: Victim (4.0) | | ActivitiesCount > 1: Predator (2.0) | RelationshipCount > 0: Victim (10.0) DesensitizationCount > 35: Predator (11.0/1.0)

11. Predator vs. Victim Patterns

12. Categorization Experiments • Second Experiment • Class: {PJ , Non-PJ} • 31 instances, 14 PJ Transcripts, 15 Non-PJ • Non-PJ obtained from Dr. Susan Gauch – collected during her ChatTrack project • PJ transcripts, both Victim and Predator were coded • Same eight attributes

13. Results • Classifier: C4.5 (J48 in Weka) • 3-fold cross validation • Success Rate: 93% • baseline 48% • Confusion matrix

14. Non PJ vs. PJ

15. Clustering Experiments • All 288 PJ Transcripts • K Means Clustering • Same eight attributes • column normalized • Four Clusters found • minimum intra-cluster variation • multiple runs to avoid local minima

16. Clusters Found

17. Labeling the Clusters • 60 Transcripts Analyzed Closely • Age Deception Data Categorized • Four distinct ways that deception can be achieved when communicating with others • Quantity • Quality • Relation • Manner McCornack, S.A., Levine, T.R., Solowczuk, K.A., Torres, H.I., & Campbell, D.M. (1992). When the alteration of information is viewed as deception: An empirical test of information manipulation theory. Communication Monographs, 59, 17-29. • Age data captured for all 288 transcripts

18. Age Deception Statistics

19. Type of Deception • Quantity manipulation findings • Honest predators average real age was 31 yrs old • Deceptive predators average real age was 38 yrs old • Quality manipulation findings • Average age given by deceptive predators was 27 yrs old • Relation and Manner manipulation findings • Rarely used by online sexual predators

20. Age Labeling – a bust 

21. Synergistic Activities • Content Analysis for the Web 2.0 • Misbehavior Detection Task • Pendar, Nick (2007) "Toward Spotting the Pedophile: Telling victim from predator in text chats " In The Proceedings of the First IEEE International Conference on Semantic Computing: 235-241. Irvine, California. • Study for the Termination of Online Predators (STOP) • Hughes, D., P. Rayson, J. Walkerdine, K. Lee, P. Greenwood, A. Rashid, C. MayChahal, and M. Brennan. 2008. Supporting Law Enforcement in Digital Communities through Natural Language Analysis,. In the proceedings of the 2nd International Workshop on Computational Forensics (IWCF’08). Washington D.C., USA, August 2008. • Isis – Protecting Children in Online Social Networks

22. Where are we going? • Data remains a big problem • PJ data is problematic • Access to large chat or “chat-like” collections is hard to get • Labeling is a bigger problem • Finding predatory chat is a “needle in haystack” problem • Applications are nice, but applications need to be grounded in text mining and communicative theory research.

23. Acknowledgements • Amanda Leatherman • Lynne Edwards • Kristina Moore • Brian D. Davison and students at Lehigh Univ. • Ursinus College • Media and Communication Studies faculty and students • Mathematics and Computer Science faculty and students • Text Mining Workshop organizers and reviewers

24. Contact Information April Kontostathis Ursinus College akontostathis@ursinus.edu http://webpages.ursinus.edu/akontostathis 610-409-3000 x2650