Nigerian Salafi Discourse Corpus (NSDC): Construction, Methods, and Use

This paper documents the construction of the Nigerian Salafi Discourse Corpus (NSDC), a purpose-built corpus of 5,791 Facebook posts (2,485,963 words; 2,518,453 tagged tokens) published by Nigerian pages and profiles over a ten-year frame (2015-2025). The NSDC was assembled from eight keyword-filtered exports of the Meta Content Library and processed through a fully documented, machine-verified pipeline executed by Hermes Agent, an agentic artificial-intelligence research assistant. The pipeline performs schema validation, three-pass deduplication, Unicode normalisation and noise removal, SGML-annotated corpus assembly, cross-keyword merging, and calendar-year segmentation. The corpus is annotated in three layers: Penn Treebank part-of-speech and lemma tagging; assignment of tokens to fifteen purpose-built semantic fields; and sentence-level coding of fifteen discourse strategies under the discourse-historical approach. This paper reports aggregate statistics, data-quality challenges and solutions, verification outcomes, a manual reliability audit of the annotation layers, and research applications.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-06
DOI
https://doi.org/10.5281/zenodo.22545300
Primary Topic
Natural Language Processing Techniques
Type
preprint
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preprint

Nigerian Salafi Discourse Corpus (NSDC): Construction, Methods, and Use

Idris Ayomo Oke
Zenodo (CERN European Organization for Nuclear Research)
Natural Language Processing Techniques
preprint

Nigerian Salafi Discourse Corpus (NSDC): Construction, Methods, and Use

Idris Ayomo Oke
preprint en

Abstract

This paper documents the construction of the Nigerian Salafi Discourse Corpus (NSDC), a purpose-built corpus of 5,791 Facebook posts (2,485,963 words; 2,518,453 tagged tokens) published by Nigerian pages and profiles over a ten-year frame (2015-2025). The NSDC was assembled from eight keyword-filtered exports of the Meta Content Library and processed through a fully documented, machine-verified pipeline executed by Hermes Agent, an agentic artificial-intelligence research assistant. The pipeline performs schema validation, three-pass deduplication, Unicode normalisation and noise removal, SGML-annotated corpus assembly, cross-keyword merging, and calendar-year segmentation. The corpus is annotated in three layers: Penn Treebank part-of-speech and lemma tagging; assignment of tokens to fifteen purpose-built semantic fields; and sentence-level coding of fifteen discourse strategies under the discourse-historical approach. This paper reports aggregate statistics, data-quality challenges and solutions, verification outcomes, a manual reliability audit of the annotation layers, and research applications.

Zenodo (CERN European Organization for Nuclear Research)
Ekiti State University (NG), University of Ibadan (NG), Adekunle Ajasin University (NG), PharmAccess Group (NL), Inaccess (Greece) (GR), Obafemi Awolowo University (NG)
Natural Language Processing Techniques
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