A Content Analysis of Journal Articles Using the Language Network Analysis Methods

Soo-Sang Lee

doi:10.3743/kosim.2014.31.4.049

December 30, 2014Journal of the Korean Society for information ManagementOpen Access

A Content Analysis of Journal Articles Using the Language Network Analysis Methods

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Key Points

To identify and characterize the foundational methodological framework of language network analysis used in published academic literature.
Conducted a systematic content analysis of 53 domestic peer-reviewed journal articles retrieved from Korean academic databases.
Categorized and evaluated methodological components: source text types, keyword selection procedures, co-occurrence criteria, network structures, software tools, and analytical metrics.
Academic papers and interview transcripts serve as the predominant text sources, with keywords extracted primarily via term frequency in main bodies and linked through co-occurrence frequency.
Studies predominantly construct multiple interrelated networks rather than single networks, frequently utilizing NetMiner, UCINET/NetDraw, NodeXL, and Pajek.
Structural network properties are routinely evaluated using metrics such as density, centrality indices, and sub-network partitioning.

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Implication

Content analysis reveals common text processing and metric choices across 53 journal articles, highlighting foundational standards for semantic network research.

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Soo-Sang Lee (2014) studied this question.

synapsesocial.com/papers/6a7009a6ce524a4339c3d433 https://doi.org/10.3743/kosim.2014.31.4.049

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A Content Analysis of Journal Articles Using the Language Network Analysis Methods

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A Content Analysis of Journal Articles Using the Language Network Analysis Methods

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