Mining bipartite graphs to improve semantic pedophile activity detection

Peer-to-peer (P2P) networks are popular to exchange large volumes of data through the Internet. Pedophile activity is a very important topic for our society and some works have recently attempted to gauge the extent of pedophile exchanges on P2P networks. A key issue is to obtain an efficient detection tool, which may decide if a sequence of keywords is related to the topic or not. We propose to use social network analysis in a large dataset from a P2P network to improve a state-of-the-art filter for pedophile queries. We obtain queries and thus combinations of words which are not tagged by the filter but should be. We also perform some experiments to explore if the original four categories of paedophile queries were to be found by topological measures only.