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计算与定性:分析在Covid-19危机期间识别网络框架的不同方法

Hossein Kermani¹, Alireza Bayat Makou², Amirali Tafreshi³

¹Political communication group, Department of communication, University of Vienna, Vienna, Austria.

International journal of social research methodology

|June 13, 2024

概括

自动化文本分析,如隐性迪里克莱特分配 (LDA),有效地识别出大型数据集中的突出的网络,但与不那么主导的斗. 人类的定性解释对深入的意义至关重要.

科学领域:

计算社会科学计算社会科学
沟通研究沟通研究
数字人文学科数字人文学科

背景情况:

在沟通研究中,自动化文本分析越来越多地被采用.
对自动化框架分析,特别是网络框架的理解有限.
需要对计算方法与定性方法进行评估.

研究的目的:

为了比较隐性迪里克莱特分配 (LDA) 与用于框架分析的人为驱动的定性编码的优缺点.
评估网络的自动检测的有效性.
评估LDA在冠状病毒危机期间对大规模推特数据的表现.

主要方法:

隐性迪里克莱特分配 (LDA) 和定性编码的比较分析.
利用了来自伊朗推特圈的4,165,177条推特 (2020年1月至4月) 的数据集.
基于数据集大小和词汇语义的检查性能.

主要成果:

LDA可靠地识别出突出的网络框架,但错过了不那么主导的框架.
在较大的数据集和更丰富的词汇语义上,LDA表现出更好的性能.
自动化方法提供了主要的见解,但人类的解释对于细微的理解至关重要.

结论:

自动化框架分析,特别是LDA,对于在大型数据集中识别主要主题非常有价值.
定性解释对于揭示更深层次的含义和不那么突出的框架是不可或缺的.
结合自动化和定性方法的混合方法提供了最全面的框架分析.

关键词:

伊朗伊朗伊朗伊朗隐藏的迪里克莱特分配 (LDA)主题建模主题建模自动化文本分析系统冠状病毒冠状病毒病毒框架分析分析框架分析分析定性分析是一种定性分析.在Twitter上,我们可以看到Twitter.

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