Combining Evidence for Social Situation Detection

Social Situations (SSs) are social context models indicating nary social interaction on small spatio-temporal scales detected by means of Social Signal Processing (SSP). We discuss the problem of how to combine evidence from several sensor-sources for the benefit of algorithmic assessment of SS in a distributed agent-based Social Networking scenario. We propose a solution based on Subjective Logic (SL) that mediates between exchanging & processing of (a) raw low level sensor data, of (b) intermediate results of 'sub-symbolic' probabilistic models typically used for SSP, and of (c) the final 'symbolic' SS models. We evaluate key aspects of the approach on the basis of a social experiment, combining audio-based and geometry-of-interaction-based methods for SS detection.

[1]  Adrian Furnham,et al.  Social Situations by Michael Argyle , 1981 .

[2]  S. Ferrari,et al.  Demining sensor modeling and feature-level fusion by Bayesian networks , 2006, IEEE Sensors Journal.

[3]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[4]  Jeff A. Bilmes,et al.  Conversation detection and speaker segmentation in privacy-sensitive situated speech data , 2007, INTERSPEECH.

[5]  Georg Groh,et al.  Deducing evidence for social situations from dynamic geometric interaction data , 2011, Int. J. Soc. Comput. Cyber Phys. Syst..

[6]  Y. Zhang,et al.  Active and dynamic information fusion for multisensor systems with dynamic bayesian networks , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Audun Jøsang,et al.  Exploring Different Types of Trust Propagation , 2006, iTrust.

[8]  Pan Hui,et al.  Distributed community detection in delay tolerant networks , 2007, MobiArch '07.

[9]  S. Sitharama Iyengar,et al.  Distributed Sensor Networks — a Review of Recent Research , 2001, J. Frankl. Inst..

[10]  Maja Pantic,et al.  Social signal processing: Survey of an emerging domain , 2009, Image Vis. Comput..

[11]  Arthur P. Dempster,et al.  A Generalization of Bayesian Inference , 1968, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[12]  Audun Jøsang,et al.  The consensus operator for combining beliefs , 2002, Artif. Intell..

[13]  Georg Groh,et al.  Detecting Social Situations from Interaction Geometry , 2010, 2010 IEEE Second International Conference on Social Computing.

[14]  Audun Jøsang,et al.  A Logic for Uncertain Probabilities , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[15]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[16]  Jeffrey S. Rosenschein,et al.  Rules of Encounter - Designing Conventions for Automated Negotiation among Computers , 1994 .

[17]  Alex Pentland,et al.  Modeling Social Diffusion Phenomena using Reality Mining , 2009, AAAI Spring Symposium: Human Behavior Modeling.

[18]  Klaus Diepold,et al.  A Trust Model for Intervehicular Communication Based on Belief Theory , 2010, 2010 IEEE Second International Conference on Social Computing.

[19]  S. Iyengar,et al.  Multi-Sensor Fusion: Fundamentals and Applications With Software , 1997 .

[20]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[21]  Georg Groh,et al.  Groups and Group-Instantiations in Mobile Communities - Detection, Modeling and Applications , 2007, ICWSM.

[22]  L. Hubert,et al.  Comparing partitions , 1985 .

[23]  Sonja Buchegger,et al.  PeerSoN: P2P social networking: early experiences and insights , 2009, SNS '09.

[24]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[25]  M. Degroot.  Reaching a Consensus , 1974 .

[26]  Daniel Gatica-Perez,et al.  Automatic nonverbal analysis of social interaction in small groups: A review , 2009, Image Vis. Comput..

[27]  Ciro Cattuto,et al.  Evaluating similarity measures for emergent semantics of social tagging , 2009, WWW '09.

[28]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[29]  Audun Jøsang.  Cumulative and Averaging Fission of Beliefs , 2010, Inf. Fusion.

[30]  Henry A. Kautz,et al.  Capturing Spontaneous Conversation and Social Dynamics: A Privacy-Sensitive Data Collection Effort , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[31]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[32]  Audun Jøsang.  Probabilistic Logic under Uncertainty , 2007, CATS.

[33]  M. Newman.  Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Georg Groh,et al.  APPLICATIONS FOR SOCIAL SITUATION MODELS , 2010 .

[35]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[36]  D. Lazer,et al.  Inferring Social Network Structure using Mobile Phone Data , 2006 .

[37]  Jeff A. Bilmes,et al.  Towards the automated social analysis of situated speech data , 2008, UbiComp.

[38]  Oliver Brdiczka,et al.  Automatic detection of interaction groups , 2005, ICMI '05.

[39]  John G. Breslin,et al.  Social Semantic Web , 2009, Handbook of Semantic Web Technologies.

[40]  Adrian Furnham,et al.  Social Situations: Language and speech , 1981 .