Mining Bulletin Board Systems Using Community Generation

Abstract

Bulletin board system (BBS) is popular on the Internet. This paper attempts to identify communities of interest-sharing users on BBS. First, the paper formulates a general model for the BBS data, consisting of a collection of user IDs described by two views to their behavior actions along the timeline, i.e., the topics of the posted messages and the boards to which the messages are posted. Based on this model which contains no explicit link information between users, a uni-party data community generation algorithm called ISGI is proposed, which employs a specifically designed hierarchical similarity function to measure the correlations between two different individual users. Then, the BPUC algorithm is proposed, which uses the generated communities to predict users’ behavior actions under certain conditions for situation awareness or personalized services development. For instance, the BPUC predictions may be used to answer questions such as “what will be the likely behavior user X may take if he/she logs into the BBS tomorrow?”. Experiments on a large scale, real-world BBS data set demonstrate the effectiveness of the proposed model and algorithms.