Modeling and predicting personal information dissemination behavior

In this paper, we propose a new way to automatically model and predict human behavior of receiving and disseminating information by analyzing the contact and content of personal communications. A personal profile, called CommunityNet, is established for each individual based on a novel algorithm incorporating contact, content, and time information simultaneously. It can be used for personal social capital management. Clusters of CommunityNets provide a view of informal networks for organization management. Our new algorithm is developed based on the combination of dynamic algorithms in the social network field and the semantic content classification methods in the natural language processing and machine learning literatures. We tested CommunityNets on the Enron Email corpus and report experimental results including filtering, prediction, and recommendation capabilities. We show that the personal behavior and intention are somewhat predictable based on these models. For instance, "...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Xiaodan Song, Ching-Yung Lin, Belle L. Tseng, Ming-Ting Sun
• Keywords: Data Mining, KDD 2005, Personal Social Capital, Personal Social Network, Social Network-based Predictions