Item Details

Exploring the use of machine learning to automate the qualitative coding of church-related tweets

Issue: Vol 14 No. 2 (2019)

Journal: Fieldwork in Religion

Subject Areas: Religious Studies Linguistics

DOI: 10.1558/firn.39789


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, mean that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interact with, and talk 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.

Author: Anthony-Paul Cooper, Emmanuel Awuni Kolog, Erkki Sutinen

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