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Study of Pauline Epistles in the New Testament Using Machine Learning

Author(s): Wei Hu

Journal: Sociology Mind
ISSN 2160-083X

Volume: 03;
Issue: 02;
Start page: 193;
Date: 2013;
Original page

Keywords: Bible | New Testament | Paul | Topic Model | Clustering | Affinity Propagation

The New Testament of the Bible is a collection of 27 books, 13 of which are attributed to St. Paul, making him a significant author of the New Testament. The Pauline letters were written to churches and individuals addressing various issues in the early church and offering instructions and encouragement to the believers of his day. To understand Paul’s message as a whole, we clustered his letters, their chapters, and their verses respectively to identify the topics that are addressed repeatedly or once, offering different views at three granularities with each complementing the other. Our calculation implied all Pauline letters could be divided into six groups, well in line with the church tradition. They were First and Second Thessalonians, First and Second Corinthians, Colossians and Ephesians, Galatians and Romans, Philemon and Philippians, and First and Second Timothy and Titus. Among all letters First Corinthians had the most unique chapters, while First Corinthians and Romans had the most unique verses. Furthermore, similar verses and chapters from his letters were also discovered. We also studied the authorship of the letter to Hebrews, an anonymous letter in the New Testament. Using known Pauline and non-Pauline letters as training data, we built nine highly reliable predictors, which collectively predicted that the book of Hebrews was not authored by Paul.
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