This paper presents the design and construction of WikiFirst, a corpus for investigating the impact of content variation on authorship similarity under a fixed genre. Prior work has investigated individual authorial style and impact of genre. However, the role of content has remained underexplored due to the lack of suitable data. We address this gap by constructing a Wikipedia-based corpus consisting exclusively of first revisions authored by non-anonymous editors, thereby ensuring high authorship certainty while maintaining a stable encyclopaedic genre.

This paper explores the extent to which stylistic features contribute to the task of authorship attribution in forensic contexts. Drawing on a filtered subset of the Enron email corpus, the study operationalizes stylistic indicators across four groups: lexical, syntactic, orthographic, and discoursal. Using R Programming Language for feature engineering and logistic regression modelling, we systematically assessed both the individual and interactive effects of these features on attribution accuracy. Results show that n-gram similarity consistently outperformed all other features, with the combined model of n-gram similarity and its interaction with other features achieving accuracy, precision and F1 scores of 91.6%, 93.3% and 91.7% respectively. The model was subsequently evaluated on a subset of the TEL corpus to assess its applicability in a forensic setting. The findings highlight the dominant role of lexical similarity and suggest that integrating interaction effects can yield further performance gains in forensic authorship analysis.