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Recent advances in information retrieval over hyperlinked corpora have convincingly demonstrated that links carry less noisy information than text. We investigate the feasibility of applying link-based methods in new applications domains. The specific application we consider is to partition authors into opposite camps within a given topic in the context of newsgroups. A typical newsgroup posting consists of one or more quoted lines from another posting followed by the opinion of the author. This social behavior gives rise to a network in which the vertices are individuals and the links represent "responded-to" relationships. An interesting characteristic of many newsgroups is that people more frequently respond to a message when they disagree than when they agree. This behavior is in sharp contrast to the WWW link graph, where linkage is an indicator of agreement or common interest. By analyzing the graph structure of the responses, we are able to effectively classify people into opposite camps. In contrast, methods based on statistical analysis of text yield low accuracy on such datasets because the vocabulary used by the two sides tends to be largely identical, and many newsgroup postings consist of relatively few words of text.

The Platform for Privacy Preferences (P3P) is the most significant effort currently underway to enable web users to gain control over their private information. The designers of P3P simultaneously designed a preference language called APPEL to allow users to express their privacy preferences, thus enabling automatic matching of privacy preferences against P3P policies. Unfortunately subtle interactions between P3P and APPEL result in serious problems when using APPEL: Users can only directly specify what is unacceptable in a policy, not what is acceptable; simple preferences are hard to express; and writing APPEL preferences is error prone. We show that these problems follow from a fundamental design choice made by APPEL, and cannot be solved without completely redesigning the language. Therefore we explore alternatives to APPEL that can overcome these problems. In particular, we show that XPath serves quite nicely as a preference language and solves all the above problems. We identify the minimal subset of XPath that is needed, thus allowing matching programs to potentially use a smaller memory footprint. We also give an APPEL to XPath translator that shows that XPath is as expressive as APPEL.