ABSTRACT

Despite the increasing adaption of automated text analysis in communication studies, its strengths and weaknesses in framing analysis are so far unknown. Fewer efforts have been made to automatic detection of networked frames. Drawing on the recent developments in this field, we harness a comparative exploration, using Latent Dirichlet Allocation (LDA) and a human-driven qualitative coding process on three different samples. Samples were comprised of a dataset of 4,165,177 million tweets collected from Iranian Twittersphere during the Coronavirus crisis, from 21 January, 2020 to 29 April, 2020. Findings showed that while LDA is reliable in identifying the most prominent networked frames, it misses to detects less dominant frames. Our investigation also confirmed that LDA works better on larger datasets and lexical semantics. Finally, we argued that LDA could give us some primary intuitions, but qualitative interpretations are indispensable for understanding the deeper layers of meaning.

摘要

尽管在传播研究中越来越多地采用自动文本分析，但其在框架分析中的优缺点目前尚不清楚。对网络帧的自动检测所做的努力较少。借鉴该领域的最新发展，我们利用潜在狄利克雷分配 (LDA) 和人类驱动的定性编码过程对三个不同样本进行了比较探索。样本由 2020 年 1 月 21 日至 2020 年 4 月 29 日冠状病毒危机期间从伊朗 Twittersphere 收集的 41651.77 亿条推文的数据集组成。调查结果表明，虽然 LDA 在识别最突出的网络框架方面是可靠的，但它未能检测到较少的主导框架。我们的调查还证实，LDA 在更大的数据集和词汇语义上效果更好。最后，