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2008
Springer

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

8 years 1 months ago
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...
Yingjie Zhou, Malik Magdon-Ismail, William A. Wall
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2008
Where ISI
Authors Yingjie Zhou, Malik Magdon-Ismail, William A. Wallace, Mark K. Goldberg
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