Supporting Law Enforcement in Digital Communities through Natural Language Analysis

Authors:
Danny Hughes;Paul Rayson;James Walkerdine;Kevin Lee;Phil Greenwood;Awais Rashid;Corinne May-Chahal;Margaret Brennan
Affiliations:
Computing, InfoLab 21, South Drive, Lancaster University, Lancaster, UK LA1 4WA;Computing, InfoLab 21, South Drive, Lancaster University, Lancaster, UK LA1 4WA;Computing, InfoLab 21, South Drive, Lancaster University, Lancaster, UK LA1 4WA;Isis Forensics, Lancaster, UK LA1 9ED;Computing, InfoLab 21, South Drive, Lancaster University, Lancaster, UK LA1 4WA;Computing, InfoLab 21, South Drive, Lancaster University, Lancaster, UK LA1 4WA;Department of Applied Social Science, Lancaster University, Lancaster, UK LA1 4YL;Child Exploitation and Online Protection Centre, London, UK SW1V 2WG
Venue:
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Year:
2008

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Abstract

Recent years have seen an explosion in the number and scale of digital communities (e.g. peer-to-peer file sharing systems, chat applications and social networking sites). Unfortunately, digital communities are host to significant criminal activity including copyright infringement, identity theft and child sexual abuse. Combating this growing level of crime is problematic due to the ever increasing scale of today's digital communities. This paper presents an approach to provide automated support for the detection of child sexual abuse related activities in digital communities. Specifically, we analyze the characteristics of child sexual abuse media distribution in P2P file sharing networks and carry out an exploratory study to show that corpus-based natural language analysis may be used to automate the detection of this activity. We then give an overview of how this approach can be extended to police chat and social networking communities.