Authorship Verification of the Deutero-Pauline Letters through Deep Learning

In the Christian tradition, fourteen letters of the New Testament have been attributed to the Apostle Paul. However, for seven of these letters—1 and 2 Timothy, Titus, Ephesians, Colossians, 2 Thessalonians, and Hebrews —the attribution to Paul has been the subject of scholarly debate. This study aims to develop a bidirectional long short-term memory (BiLSTM) network to classify chunks of these disputed letters, each approximately 100 words long, as either authored by Paul or not. Two model variants—a plaintext variant and a lemmatized text variant—were trained on undisputed Pauline letters and ‘impostor letters’ which serve as negative examples of Paul’s writing. The plaintext variant achieved 84% accuracy and the lemmatized text variant 83% accuracy. Both variants classify the majority of text chunks from Colossians and 2 Thessalonians as Pauline and the majority of chunks from Hebrews and 1 Timothy as non-Pauline, although caution is warranted in drawing strong conclusions from these results. For the remaining disputed Pauline letters—Titus, 2 Timothy, and Ephesians—the majority classification varies between the model variants, further emphasizing the need for caution. Nevertheless, this study introduces a deep learning approach to the authorship verification problem of the disputed Pauline letters, potentially serving as a model for future research.

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Publication Details

Journal
HIPHIL Novum
Published
2025-06-06
DOI
https://doi.org/10.7146/hn.v10i1.147482
Primary Topic
Authorship Attribution and Profiling
Type
article
Field-Weighted Citation Impact
0.00

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article

Authorship Verification of the Deutero-Pauline Letters through Deep Learning

Rianne De Heide, Evy Beijen
HIPHIL Novum
Authorship Attribution and Profiling
article

Authorship Verification of the Deutero-Pauline Letters through Deep Learning

Rianne De Heide, Evy Beijen
article en

Abstract

In the Christian tradition, fourteen letters of the New Testament have been attributed to the Apostle Paul. However, for seven of these letters—1 and 2 Timothy, Titus, Ephesians, Colossians, 2 Thessalonians, and Hebrews —the attribution to Paul has been the subject of scholarly debate. This study aims to develop a bidirectional long short-term memory (BiLSTM) network to classify chunks of these disputed letters, each approximately 100 words long, as either authored by Paul or not. Two model variants—a plaintext variant and a lemmatized text variant—were trained on undisputed Pauline letters and ‘impostor letters’ which serve as negative examples of Paul’s writing. The plaintext variant achieved 84% accuracy and the lemmatized text variant 83% accuracy. Both variants classify the majority of text chunks from Colossians and 2 Thessalonians as Pauline and the majority of chunks from Hebrews and 1 Timothy as non-Pauline, although caution is warranted in drawing strong conclusions from these results. For the remaining disputed Pauline letters—Titus, 2 Timothy, and Ephesians—the majority classification varies between the model variants, further emphasizing the need for caution. Nevertheless, this study introduces a deep learning approach to the authorship verification problem of the disputed Pauline letters, potentially serving as a model for future research.

HIPHIL NovumVol. 10(1)
Vrije Universiteit Amsterdam (NL), University of Twente (NL)
Vrije Universiteit Amsterdam
Openalex Percentile: Top 94%
Authorship Attribution and Profiling
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