Human communication as coupled time series: Quantifying multi-participant recurrence

Angus, Daniel, Smith, Andrew E. and Wiles, Janet (2012) Human communication as coupled time series: Quantifying multi-participant recurrence. IEEE Transactions on Audio, Speech, and Language Processing, 20 6: 1795-1807.


Author Angus, Daniel
Smith, Andrew E.
Wiles, Janet
Title Human communication as coupled time series: Quantifying multi-participant recurrence
Journal name IEEE Transactions on Audio, Speech, and Language Processing  (ERA 2012 Listed)    (ERA 2010 Rank A*)   Check publisher's open access policy
Publication date 2012-08
Sub-type Article
DOI 10.1109/TASL.2012.2189566
Volume number 20
Issue number 6
ISSN 1558-7916; 1558-7924
Start page 1795
End page 1807
Total pages 13
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2013
Language eng
Abstract Human communication is more than just the transmission of information. It also involves complex interaction dynamics that reflect the roles and communication styles of the participants. A novel approach to studying human communication is to view conversation as a coupled time series and apply analysis techniques from dynamical systems to the recurring topics or concepts. In this paper, we define a set of metrics that enable quantification of the complex interaction dynamics visible in conceptual recurrence. These multi-participant recurrence (MPR) metrics can be seen as an extension of recurrence quantification analysis (RQA) into the symbolic domain. This technique can be used to monitor the state of a communication system and inform about interaction dynamics, including the level of topic consistency between participants; the timing of state changes for the participants as a result of changes in topic focus; and, patterns of topic proposal, reflection, and repetition. We demonstrate three use studies applying the new metrics to conversation transcripts from different genres to demonstrate their ability to characterize individual communication participants and intergroup communication patterns.
Keyword Concept learning
Discourse
Recurrences and difference equations
Text analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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