A Generative Model for Statistical Determination of Information Content from Conversation Threads

We present a generative model for determining the information content of a message without analyzing the message content. Such a tool is useful for automated analysis of the vast contents of online communication which are extensively contaminated by uninformative, spam, and broadcast. Content analysis is not feasible in such a setting. We propose a purely statistical methodology to determine the information value of a message, which we denote the Information Content Factor (ICF). Underlying our methodology is the definition of information in a message as the message's ability to generate conversation. The generative nature of our model allows us to estimate the ICF of a message without prior information on the participants. We test our approach by applying it to separating spam/broadcast messages from non-spam/non-broadcast. Our algorithms achieve 94% accuracy when tested against a human classifier which analyzed content. Categories and Subject Descriptors H.4.3 [Information Syst...

• Type: Journal
• Year: 2008
• Where: ISI
• Authors: Yingjie Zhou, Malik Magdon-Ismail, William A. Wallace, Mark K. Goldberg
• Keywords: Information, Information Content, Information Content Factor, ISI 2008, Security Privacy