Social Network Based Reputation Computation and Document Classification

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We develop two social network based algorithms that automatically com- pute author reputation from a collection of textual documents. We first extract keyword reference behaviors of the authors to construct a social network, which represents re- lationships among the authors in terms of information reference behavior .W ith this network, we apply the two algorithms: the first computes each author's reputation value considering only direct reference and the second utilizes indirect reference recursively. We compare the reputation values computed by the two algorithms and reputation ratings given by a human domain expert. We further evaluate the algorithms in email categorization tasks by comparing them with machine learning techniques. Finally, we analyse the social network through a community detection algorithm and other analy- sis techniques. We observed several interesting phenomena including the network being scale-free and having a negative assortativity.