abstract: Internet and social media devices created a new public space for debate on political and social topics (Papacharissi 2002; Himelboim 2010). Hotly debated issues span all spheres of human activity; from liberal vs. conservative politics, to radical vs. counter-radical religious debate, to climate change debate in scientific community, to globalization debate in economics, and to nuclear disarmament debate in security. Many prominent ’camps’ have emerged within Internet debate rhetoric and practice (Dahlberg, n.d.). In this research I utilized feature extraction and model fitting techniques to process the rhetoric found in the web sites of 23 Indonesian Islamic religious organizations, later with 26 similar organizations from the United Kingdom to profile their ideology and activity patterns along a hypothesized radical/counter-radical scale, and presented an end-to-end system that is able to help researchers to visualize the data in an interactive fashion on a time line. The subject data of this study is the articles downloaded from the web sites of these organizations dating from 2001 to 2011, and in 2013. I developed algorithms to rank these organizations by assigning them to probable positions on the scale. I showed that the developed Rasch model fits the data using Andersen’s LR-test (likelihood ratio). I created a gold standard of the ranking of these organizations through an expertise elicitation tool. Then using my system I computed expert-to-expert agreements, and then presented experimental results comparing the performance of three baseline methods to show that the Rasch model not only outperforms the baseline methods, but it was also the only system that performs at expert-level accuracy. I developed an end-to-end system that receives list of organizations from experts, mines their web corpus, prepare discourse topic lists with expert support, and then ranks them on scales with partial expert interaction, and finally presents them on an easy to use web based analytic system. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016