ABSTRACT Given the scale of digital communication, researchers face a painful trade-off between powerful, scalable computational strategies, and the theoretical sensitivity offered by small-scale manual analyses. Especially in the study of natural discourse on digital media, the interactive, ever-evolving stream of conversations across multiple platforms regularly defies efforts to obtain well-defined samples of manageable size, while their linguistic variability imposes major limitations upon the accuracy of automated tools. In this paper, we draw upon recent advances in computational text analysis to develop a hybrid approach to the deductive analysis of large-scale digital discourse, which combines the algorithmic extraction of coherent, recurrent patterns with a manual coding of identified patterns. The approach scales up to treat millions of texts at minimal added human effort, while affording researchers close control over the process of theory-guided classification. We demonstrate the power of Hybrid Content Analysis by studying polarization in a quarter of a million contributions from cross-platform interactive social media discourse about a controversial incident.

中文翻译：

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混合内容分析：为大文本语料库的理论驱动，计算机辅助分类制定策略

摘要鉴于数字通信的规模，研究人员面临着强大，可扩展的计算策略与小规模人工分析提供的理论敏感性之间的痛苦折衷。尤其是在研究数字媒体的自然话语时，跨多个平台的互动性，不断发展的对话流经常会竭尽全力来获取可管理大小的定义明确的样本，而其语言可变性则对自动化工具的准确性造成了重大限制。在本文中，我们借鉴了计算文本分析的最新进展，以开发一种混合的方法来进行大规模数字话语的演绎分析，该方法将相干，重复模式的算法提取与已识别模式的手动编码相结合。该方法可以扩大规模，以最少的人力来处理数百万个文本，同时为研究人员提供对理论指导分类过程的紧密控制。通过研究跨平台交互式社交媒体话语中有争议的事件的四分之一贡献，我们通过研究极化来证明混合内容分析的力量。