document: Mining newsgroups using networks arising from social behavior 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 .
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