A Network Model of Interpersonal Alignment in Dialog
In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-cal...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2010-06-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/12/6/1440/ |
| _version_ | 1798042538160947200 |
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| author | Alexander Mehler Andy Lücking Petra Weiß |
| author_facet | Alexander Mehler Andy Lücking Petra Weiß |
| author_sort | Alexander Mehler |
| collection | DOAJ |
| description | In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. |
| first_indexed | 2024-04-11T22:36:49Z |
| format | Article |
| id | doaj.art-821db73a847442cd8aa65e77159e83ff |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T22:36:49Z |
| publishDate | 2010-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-821db73a847442cd8aa65e77159e83ff2022-12-22T03:59:11ZengMDPI AGEntropy1099-43002010-06-011261440148310.3390/e12061440A Network Model of Interpersonal Alignment in DialogAlexander MehlerAndy LückingPetra WeißIn dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations.http://www.mdpi.com/1099-4300/12/6/1440/alignment in communicationstructural couplinglinguistic networksgraph distance measuresmutual information of graphsquantitative network analysis |
| spellingShingle | Alexander Mehler Andy Lücking Petra Weiß A Network Model of Interpersonal Alignment in Dialog Entropy alignment in communication structural coupling linguistic networks graph distance measures mutual information of graphs quantitative network analysis |
| title | A Network Model of Interpersonal Alignment in Dialog |
| title_full | A Network Model of Interpersonal Alignment in Dialog |
| title_fullStr | A Network Model of Interpersonal Alignment in Dialog |
| title_full_unstemmed | A Network Model of Interpersonal Alignment in Dialog |
| title_short | A Network Model of Interpersonal Alignment in Dialog |
| title_sort | network model of interpersonal alignment in dialog |
| topic | alignment in communication structural coupling linguistic networks graph distance measures mutual information of graphs quantitative network analysis |
| url | http://www.mdpi.com/1099-4300/12/6/1440/ |
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