Positing the problem: enhancing classification of extremist web content through textual analysis

Webpages with terrorist and extremist content are key factors in the recruitment and radicalization of disaffected young adults who may then engage in terrorist activities at home or fight alongside terrorist groups abroad. This paper reports on advances in techniques for classifying data collected by the Terrorism and Extremism Network Extractor (TENE) webcrawler, a custom-written program that browses the World Wide Web, collecting vast amounts of data, retrieving the pages it visits, analyzing them, and recursively following the links out of those pages. The textual content is subjected to enhanced classification through software analysis, using the Posit textual analysis toolset, generating a detailed frequency analysis of the syntax, including multi-word units and associated part-of-speech components. Results are then deployed in a knowledge extraction process using knowledge extraction algorithms, e.g., from the WEKA system. Indications are that the use of the data enrichment through application of Posit analysis affords a greater degree of match between automatic and manual classification than previously attained. Furthermore, the incorporation and deployment of these technologies promises to provide public safety officials with techniques that can help to detect terrorist webpages, gauge the intensity of their content, discriminate between webpages that do or do not require a concerted response, and take appropriate action where warranted.