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Behavioral Entropy of a Cellular Phone User

Behavioral Entropy of a Cellular Phone User. Santi Phithakkitnukoon Husain Husna Ram Dantu (Presenter) Computer Science & Engineering University of North Texas.

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Behavioral Entropy of a Cellular Phone User

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  1. Behavioral Entropy of a Cellular Phone User Santi Phithakkitnukoon Husain Husna Ram Dantu (Presenter) Computer Science & Engineering University of North Texas First International Workshop on Social Computing, Behavioral Modeling, and Prediction, Phoenix, AZ, USA, April 1-2, 2008

  2. Introduction • Mobile phone has become an integral part of many people’s social lives. • This has had profound implications on both how people as individuals perceive communication as well as in the patterns of communication of humans as a society.

  3. How to answer the following questions with limited information like telephone call logs • Who are my friends, family, opt-ins, opt-outs ? • Who is going to call me next • When are they going to call me • Is John busy now ? • Is Bob ready to answer my call ? • Where is Mary now ? • Are there any special events in your life

  4. Contributions • We analyzed the behavior of cellular phone users and identify behavior signatures based on their calling patterns. • We quantify and infer the relationship of a person’s information entropy based on the location, time of the call, inter-connected time, and duration of the call.

  5. Randomness Level • Information Entropy (Shannon’s Entropy) • Based on H(X), we were able to quantify randomness level based on • Location of user • Time of call • Inter-connected time • Duration of call.

  6. Results and Analysis • Real-life Dataset • MIT Reality Mining Project • Call logs of 94 mobile users of 9 months • Results • Correlation Coefficient Factor Analysis

  7. Results and Analysis • Scatter plots showing relationships among H(L), H(C), H(I), and H(T) with the linear trend lines

  8. Conclusion • In this paper, we analyzed cellular phone user behavior in forms of randomness level using information entropy based on user’s location, time of call, inter-connected time, and duration of call. We are able to capture the randomness level based on the underlying parameters using the correlation coefficient and factor analysis. • Based on our study, the user’s randomness level based on location has high correlation to time of making phone calls and vice versa. Our study also shows that the randomness level based on user’s inter-connected time has a high correlation to the time spent on phone calls. • We believe that this work can also be extended to predict services suitable for the user.

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