Contextual Prediction of Communication Flow in Social Networks

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 57 - 65
• Main Authors: De Choudhury, Munmun, Sundaram, Hari, John, Ajita, Seligmann, Doree Duncan
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Computing methodologies > Machine learning > Machine learning approaches > Factorization methods > Canonical correlation analysis; Computing methodologies > Modeling and simulation > Model development and analysis; Computing methodologies > Modeling and simulation > Model development and analysis > Model verification and validation; Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Web-based interaction; Information systems > Information retrieval; Information systems > Information retrieval > Evaluation of retrieval results; Information systems > Information storage systems > Storage architectures > Storage network architectures; Mathematics of computing > Probability and statistics > Statistical paradigms > Regression analysis
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.39

The paper develops a novel computational framework for predicting communication flow in social networks based on several contextual features. The problem is important because prediction of communication flow can impact timely sharing of specific information across a wide array of communities. We determine the intent to communicate and communication delay between users based on several contextual features in a social network corresponding to (a) neighborhood context, (b) topic context and (c) recipient context. The intent to communicate and communication delay are modeled as regression problems which are efficiently estimated using Support Vector Regression. We predict the intent and the delay, on an interval of time using past communication data. We have excellent prediction results on a real-world dataset from MySpace.com with an accuracy of 13-16%. We show that the intent to communicate is more significantly influenced by contextual factors compared to the delay.