Event detection using user interaction behavior models
Artificial Intelligence Review
Effective email network visualization techniques by means of user behaviors
Intelligent Data Analysis
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Mobile social networks are gaining popularity with the pervasive use of mobile phones and other handheld devices. In these networks, users maintain friendship links, exchange short messages and share content with one another. In this paper, we study the user behaviors in mobile messaging and friendship linking using the data collected from a large mobile social network service known as myGamma (m.mygamma.com). We distinguish two types of user behaviors: soliciting active responses for an initiated message and responding to an incoming message. We propose various models for the two behaviors also known as engagingness and responsiveness. Our experiments show that the two behaviors are quite distinct from each other although they may be correlated. We also show that engaging and responsive users enjoy more friendships. Finally, we show that the engaging and responsive users participate more in messaging about major topics.