The continued growth of child pornography distribution online has resulted in the need for new innovate tools to combat the problem. Since shutting down all child exploitation (CE) websites is arguably impossible, the goal must be to find the most efficient way of identifying the key targets and then apprehend them. Using a web-crawler we specifically designed for extracting CE networks, we 1) examine the structure of ten CE networks and compare them to a control group of sports-related networks, and 2) provide a measure (network capital) that allows for better identification of the most important targets, within each network, for law enforcement purposes. Results show that network capital–a combination of content severity (images, videos, and text) and connectivity (links to other child pornography websites)–is a more reliable measure of target prioritization than traditional methods currently being used. Implications for future research and law enforcement practices are discussed.
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Thesis advisor: Bouchard, Martin
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