TY - JOUR
T1 - Exploring the use of machine learning to automate the qualitative coding of church-related tweets
AU - Cooper, Anthony Paul
AU - Kolog, Emmanuel Awuni
AU - Sutinen, Erkki
N1 - Publisher Copyright:
© Equinox Publishing Ltd 2020.
PY - 2020
Y1 - 2020
N2 - 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, Naive-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.
AB - 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, Naive-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.
KW - Digital theology
KW - Machine learning
KW - Social media research
KW - Sociology of religion
UR - http://www.scopus.com/inward/record.url?scp=85084292968&partnerID=8YFLogxK
U2 - 10.1558/FIRN.40610
DO - 10.1558/FIRN.40610
M3 - Article
AN - SCOPUS:85084292968
SN - 1743-0615
VL - 14
SP - 140
EP - 159
JO - Fieldwork in Religion
JF - Fieldwork in Religion
IS - 2
ER -