Trade-Offs in social and behavioral modeling in mobile networks

Authors:
Yaniv Altshuler;Michael Fire;Nadav Aharony;Zeev Volkovich;Yuval Elovici;Alex (Sandy) Pentland
Affiliations:
MIT Media Lab;Deutsche Telekom Lab, Department of Information Systems Engineering, Ben-Gurion University, Israel;MIT Media Lab;Department of Software Engineering, Ort Braude College, Israel;Deutsche Telekom Lab, Department of Information Systems Engineering, Ben-Gurion University, Israel;MIT Media Lab
Venue:
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Year:
2013

Citing 11
Cited 0

The link-prediction problem for social networks

Journal of the American Society for Information Science and Technology
Mobiscopes for Human Spaces

IEEE Pervasive Computing
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
Sensible organizations: technology and methodology for automatically measuring organizational behavior

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
On the leakage of personally identifiable information via online social networks

ACM SIGCOMM Computer Communication Review
You are who you know: inferring user profiles in online social networks

Proceedings of the third ACM international conference on Web search and data mining
Social sensing for epidemiological behavior change

Proceedings of the 12th ACM international conference on Ubiquitous computing
The Jigsaw continuous sensing engine for mobile phone applications

Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Discovering human places of interest from multimodal mobile phone data

Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Social fMRI: Investigating and shaping social mechanisms in the real world

Pervasive and Mobile Computing
Who is going to win the next Association for the Advancement of Artificial Intelligence Fellowship Award? Evaluating researchers by mining bibliographic data

Journal of the American Society for Information Science and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Today's smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and behavior prediction. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones. In particular, we look at the dynamic learning process over time with various sizes of sampling groups and examine the interplay between these two parameters. We validate our model using extensive simulations carried out using the "Friends and Family" dataset which contains rich data signals gathered from the smartphones of 140 adult members of a young-family residential community for over a year and is one of the most comprehensive mobile phone datasets gathered in academia to date.