A new algorithm tool could help law enforcement agencies filter out and focus on sex offenders most likely to set up face-to-face meetings with child victims.
The Chat Analysis Triage Tool (CATT) was presented by Kathryn Seigfried-Spellar, assistant professor of computer and information technology at Purdue Polytechnic Institute, during the International Association of Law Enforcement Intelligence Analysts Conference in Anaheim, California.
The FBI estimates that 750,000 adults seek sex with youths on a daily basis, and in 2015 the Internet Crimes Against Children (ICAC) task forces arrested over 60,000 internet sex offenders.
However, investigating crimes against children – specifically sexual solicitations – are complicated as some offenders are motivated by sexual fantasy chats, with others intent on persuading an underage victim into a face-to-face meeting.
CATT is said to allow officers to work through the volume of solicitations and use algorithms to examine the word usage and conversation patterns by a suspect. Seigfried-Spellar said data was taken from online conversations provided voluntarily by law enforcement agencies.
“We went through and tried to identify language-based differences and factors like self-disclosure,” she said in a statement. Self-disclosure is a tactic used by suspects who share personal stories to develop trust.
“If we can identify language differences, then the tool can identify these differences in the chats in order to give a risk assessment and a probability that this person is going to attempt face-to-face contact with the victim,” Seigfried-Spellar said. “That way, officers can begin to prioritise which cases they want to put resources toward to investigate more quickly.”
Other standout characteristics of sexual predators grooming victims for a face-to-face meeting is that the chats will often go on for weeks or even months until a meeting is achieved. Those involved in sexual fantasy chatting move on from one youth to another more quickly.
The project started as a result of a partnership with Ventura County Sheriff’s Department in California.
Seigfried-Spellar said the research discovered tactics like self-disclosure are generally used early in a predator’s talks with a potential victim.
“Meaning that we could potentially stop a sex offence from occurring because if law enforcement is notified of a suspicious chat quickly enough, CATT can analyse and offer the probability of a face-to-face,” she said. “We could potentially prevent a child from being sexually assaulted.”
Seigfried-Spellar developed CATT with associate professor Julia Taylor Rayz, who specialises in machine learning and natural language processing, and computer and information technology department head Marcus Rogers, who has a background in digital forensics tool development.
CATT algorithms examine only the conversation factors and do not take the sex of either suspect or victim into consideration, at this time.
The project began with initial research done by Seigfried-Spellar and former Purdue professor Ming Ming Chiu. The exploratory study examined over 4,300 messages in 107 online chat sessions involving arrested sex offenders, identifying different trends in word usage and self-disclosure by fantasy and contact sex offenders using statistical discourse analysis.
The trends determined through this research formed the basis for CATT. The research, “Detecting Contact vs. Fantasy Online Sexual Offenders in Chats with Minors: Statistical Discourse Analysis of Self-Disclosure and Emotion Words,” has been accepted and will be published in the journal Child Abuse and Neglect.
Initial plans are to give the tool to several law enforcement departments for tests. Seigfried-Spellar said CATT could be handling data from active cases as early as the end of the year.