- Author:
- Tanzeem Khalid Choudhury,
- Supervisor:
- Alex P. Pentland
Knowledge of how groups of people interact is important in many disciplines, e.g. organizational behavior, social network analysis, knowledge management and ubiquitous computing. Existing studies of social network interactions have either been restricted to online communities, where unambiguous measurements about how people interact can be obtained (available from chat and email logs), or have been forced to rely on questionnaires, surveys or diaries to get data on face-to-face interactions between people.
The aim of this thesis is to automatically model face-to-face interactions within a community. The first challenge was to collect rich and unbiased sensor data of natural interactions. The “sociometer”, a specially designed wearable sensor package, was built to address this problem by unobtrusively measuring face-to-face interactions between people. Using the sociometers, 1518 hours of wearable sensor data from 23 individuals was collected over a two-week period (66 hours per person).
This thesis develops a computational framework for learning the interaction structure and dynamics automatically from the sociometer data. Low-level sensor data are transformed into measures that can be used to learn socially relevant aspects of people's interactions—e.g. identifying when people are talking and whom they are talking to. The network structure is learned from the patterns of communication among people. The dynamics of a person's interactions, and how one person's dynamics affects the other's style of interaction are also modeled. Finally, a person's style of interaction is related to the person's role within the network. The algorithms are evaluated by comparing the output against hand-labeled and survey data. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Cited By
- Farrahi K and Zia K (2018). Trust reality-mining, Human-centric Computing and Information Sciences, 7:1, (1-16), Online publication date: 1-Dec-2017.
- Jeon Y, Kuk S, Kim H and Park Y Judging Dynamic Co-Existence with Smartphone Magnetometer Traces Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, (1-2)
- Julien C, Liu C, Murphy A and Picco G BLEnd Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, (105-116)
- Montanari A Multimodal Indoor Social Interaction Sensing and Real-time Feedback for Behavioural Intervention Proceedings of the 2015 Workshop on Wireless of the Students, by the Students, & for the Students, (7-9)
- Hung H, Englebienne G and Cabrera Quiros L Detecting conversing groups with a single worn accelerometer Proceedings of the 16th International Conference on Multimodal Interaction, (84-91)
- Lane N, Pengyu L, Zhou L and Zhao F Connecting personal-scale sensing and networked community behavior to infer human activities Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, (595-606)
- Rossi M, Amft O, Feese S, Käslin C and Tröster G MyConverse Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, (1275-1284)
- Tripathi P and Burleson W Predicting creativity in the wild Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, (1203-1212)
- Rahman M, Ali A, Plarre K, al'Absi M, Ertin E and Kumar S mConverse Proceedings of the 2nd Conference on Wireless Health, (1-10)
- Madan A, Farrahi K, Gatica-Perez D and Pentland A Pervasive sensing to model political opinions in face-to-face networks Proceedings of the 9th international conference on Pervasive computing, (214-231)
- Wyatt D, Choudhury T, Bilmes J and Kitts J (2011). Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science, ACM Transactions on Intelligent Systems and Technology (TIST), 2:1, (1-41), Online publication date: 1-Jan-2011.
- Escalera S, Radeva P, Vitrià J, Baró X and Raducanu B Modelling and analyzing multimodal dyadic interactions using social networks International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, (1-8)
- Lu H, Pan W, Lane N, Choudhury T and Campbell A SoundSense Proceedings of the 7th international conference on Mobile systems, applications, and services, (165-178)
- Nakakura T, Sumi Y and Nishida T Neary Proceedings of the 10th workshop on Mobile Computing Systems and Applications, (1-6)
- Nishimura J and Kuroda T Speaker recognition using speaker-independent universal acoustic model and synchronous sensing for "business microscope" Proceedings of the 4th international conference on Wireless pervasive computing, (304-308)
- Olguín D, Waber B, Kim T, Mohan A, Ara K and Pentland A (2009). Sensible organizations, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39:1, (43-55), Online publication date: 1-Feb-2009.
- Dutta P and Culler D Practical asynchronous neighbor discovery and rendezvous for mobile sensing applications Proceedings of the 6th ACM conference on Embedded network sensor systems, (71-84)
- Wyatt D, Choudhury T and Bilmes J Learning hidden curved exponential family models to infer face-to-face interaction networks from situated speech data Proceedings of the 23rd national conference on Artificial intelligence - Volume 2, (732-738)
- Otsuka K, Sawada H and Yamato J Automatic inference of cross-modal nonverbal interactions in multiparty conversations Proceedings of the 9th international conference on Multimodal interfaces, (255-262)
- Katagiri Y, Bono M and Suzuki N Conversational inverse information for context-based retrieval of personal experiences Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence, (365-376)
- Pentland A Socially aware media Proceedings of the 13th annual ACM international conference on Multimedia, (690-695)
- Pentland A (2019). Socially Aware Computation and Communication, Computer, 38:3, (33-40), Online publication date: 1-Mar-2005.
- Madan A, Caneel R and Pentland A GroupMedia Proceedings of the 6th international conference on Multimodal interfaces, (309-316)
- Pentland A (2004). Learning Communities — Understanding Information Flow in Human Networks, BT Technology Journal, 22:4, (62-70), Online publication date: 1-Oct-2004.
Index Terms
- Sensing and modeling human networks
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