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.