Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets
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...
| Authors: | ; ; |
|---|---|
| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
|
| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
Equinox
[2019]
|
| In: |
Fieldwork in religion
Year: 2019, Volume: 14, Issue: 2, Pages: 140-159 |
| Standardized Subjects / Keyword chains: | B
Church
/ Online community
/ Twitter (Softwareplattform)
/ New media
/ Artificial intelligence
/ Algorithms
/ Communication
/ Quality improvement
|
| IxTheo Classification: | CB Christian life; spirituality CD Christianity and Culture CF Christianity and Science FD Contextual theology |
| Further subjects: | B
social media research
B digital theology B sociology of religion B Machine Learning |
| Online Access: |
Volltext (doi) |
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| 520 | |a 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. | ||
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