Socio-Semantic Network Motifs Framework for Discourse Analysis

Bodong Chen, Xinran Zhu, Hong Shui
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引用次数: 6

Abstract

Effective collaborative discourse requires both cognitive and social engagement of students. To investigate complex socio-cognitive dynamics in collaborative discourse, this paper proposes to model collaborative discourse as a socio-semantic network (SSN) and then use network motifs – defined as recurring, significant subgraphs – to characterize the network and hence the discourse. To demonstrate the utility of our SSN motifs framework, we applied it to a sample dataset. While more work needs to be done, the SSN motifs framework shows promise as a novel, theoretically informed approach to discourse analysis.
语篇分析的社会语义网络母题框架
有效的协作话语需要学生的认知和社会参与。为了研究协作语篇中复杂的社会认知动态,本文建议将协作语篇建模为社会语义网络(SSN),然后使用网络基序(定义为重复出现的、重要的子图)来表征网络和语篇。为了演示SSN主题框架的效用,我们将其应用于一个示例数据集。虽然需要做更多的工作,SSN母题框架显示出作为一种新颖的、理论上的话语分析方法的希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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