Kyle Christensen

A Hierarchical Algorithm for Clustering Extremist Web Pages

Extremist political movements have proliferated on the web in recent years due to the advent of minimal publication costs coupled with near universal access, resulting in what appears to be an abundance of groups that hover on the fringe of many socially divisive issues. Whether white-supremacist, neo- Nazi, anti-abortion, black separatist, radical Christian, animal rights, or violent environmentalists, all have found a home (and voice) on the Web. These groups form social networks whose ties are predicated primarily on shared political goals. Little is known about these groups, their interconnections, their animosities, and most importantly, their growth and development and studies such as the Dark Web Project, while considering domestic extremists, have focused primarily on international terrorist groups. Yet here in the US, there has been a complex social dynamic unfolding as well. While left-wing radicalism declined throughout the 80s and 90s, right wing hate groups began to flourish. Today, the web offers a place for any brand of extremism, but little is understood about their current growth and development. While there is much to gain from in-depth studies of the content provided by these sites, there is also a surprising amount of information contained in their online network structure as manifested in links between and among these web sites. Our research follows the idea that much can be known about you by the company you keep. In this paper, we propose an approach to measure the intrinsic relationships (i.e., similarities) of a set of extremist web pages. In this model, the web presence of a group is thought of as a node in a social network and the links between these pages are the ties between groups. This approach takes the bi-directional hyperlink structure of web pages and, based on similarity scores, applies an effective multi-membership clustering algorithm known as the quasi clique merger method to cluster these web pages using a derived hierarchical tree. The experimental results show that this new similarity measurement and hierarchical clustering algorithm gives an improvement over traditional link based clustering methods.

Modeling Network Changes: Systemic Centrality in Foreign Policy Interaction Analysis

The complex network of relationships between countries provides an abundance of data from which intelligence analysts must synthesize useful interpretations that effectively inform foreign policy and security decision making. In this work, a network of foreign policy event interactions is developed and then used to describe the state of the international system by estimating the behavioral distances from all relevant actors to the US. Using a graph theoretic approach, we develop a tool for estimating behavioral distances through direct network links when behavior is present, and indirect network paths when direct behavior is unobserved. The international system is examined in temporal aggregations of 60 and 30 days prior to and following the 1991 Gulf War and the 2003 invasion of Iraq. Both periods see distancing from the US as the wars approach, and a partial return to the prior state by 60 days after the initiation of conflict. The overall position of the US and the response of other nations towards the US indicates that the systemic leadership role of the US is diminished in behavioral relationships with a moderate number of actors.