Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets
| العنوان: | Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets |
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| المؤلفون: | Erkki Sutinen, Anthony-Paul Cooper, Emmanuel Awuni Kolog |
| المصدر: | Fieldwork in Religion. 14:140-159 |
| بيانات النشر: | Equinox Publishing, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Computer science, business.industry, Religious studies, Artificial intelligence, Machine learning, computer.software_genre, business, computer, Coding (social sciences) |
| الوصف: | 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. |
| تدمد: | 1743-0623 1743-0615 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::795dc4a3e77b47aa175646ee8a512204Test https://doi.org/10.1558/firn.40610Test |
| رقم الانضمام: | edsair.doi.dedup.....795dc4a3e77b47aa175646ee8a512204 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17430623 17430615 |
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