Textual network analysis: Detecting prevailing themes and biases in international news and social media
Textual network analysis: Detecting prevailing themes and biases in international news and social media
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DOI：
10.1111/soc4.12779
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发表时间：
2020-02-14
影响因子：
2.7
通讯作者：
Segev, Elad
中科院分区：
文献类型：
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作者：
Segev, Elad
当今可用的信息量不断增加，为社会科学家提供了机遇，但也带来了挑战。本文提出了文本网络分析——一种网络分析程序，可将任何给定的文本转换为同时出现的单词的视觉图。它针对不熟悉网络分析的学者和学生，逐步展示如何使用该方法并强调其优点和应用。它演示了如何识别文本中出现的主要主题以及检测其偏见和框架。研究人员可以使用此过程作为基础内容分析来制定理论或作为测试现有假设的基础。论文的第二部分介绍了两项应用文本网络分析的研究：(a) 识别精英报纸在“假新闻”话语中提出的主题；(b) 在 Twitter 上绘制与中国相关的主题。这两个例子都表明文本网络分析如何与传播学、国际关系和政治学学者以及希望了解流行话语并更有效地调整其信息的从业者相关。
The growing volumes of information available today provide opportunities but also challenges for social scientists. This paper presents textual network analysis-a network analysis procedure that transforms any given text into a visual map of words co-occurring together. It aims at scholars and students who are not familiar with network analysis, showing step-by-step how to use this approach and highlighting its advantages and applications. It demonstrates how to identify the main themes appearing in the text as well as to detect its biases and frames. Researchers can use this procedure as a grounded content analysis to formulate theories or as a basis to test existing hypotheses. The second part of the paper presents two studies that applied textual network analysis: (a) to identify the main themes raised by elite newspapers on the "fake news" discourse and (b) to map the topics related to China on Twitter. Both examples show how textual network analysis can be relevant for communication, international relations, and political science scholars as well as for practitioners, wishing to understand the prevailing discourses and tailor their messages more effectively.