This
article builds on previous research around the exploration of the
content of church-related tweets. It does so by exploring whether the
qualitative thematic coding of such tweets can, in part, be automated by
the use of machine learning. It compares three supervised machine
learning algorithms to understand how useful each algorithm is at a
classification task, based on a dataset of human-coded church-related
tweets. The study finds that one such algorithm, Naïve-Bayes, performs
better than the other algorithms considered, returning Precision, Recall
and F-measure values which each exceed an acceptable threshold of 70%.
This has far-reaching consequences at a time where the high volume of
social media data, in this case, Twitter data, means that the
resource-intensity of manual coding approaches can act as a barrier to
understanding how the online community interacts with, and talks about,
church. The findings presented in this article offer a way forward for
scholars of digital theology to better understand the content of online
church discourse.