# Document Provenance and Authentication through Authorship Classification

arXiv: https://arxiv.org/abs/2303.01197 · HTML: https://mlsanity.com/paper/2303.01197/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Natural Disasters Detection in Social Media and Satellite imagery: a survey](https://mlsanity.com/paper/1901.04277/) (2019)
- [Social Media as an Instant Source of Feedback on Water Quality](https://mlsanity.com/paper/2202.04462/) (2022)

## Built on by (most cited first, 4 of 4)

- [Stylometry Analysis of Multi-authored Documents for Authorship and Author Style Change Detection](https://mlsanity.com/paper/2401.06752/) (2024)
- [Stylometry Analysis of Human and Machine Text for Academic Integrity](https://mlsanity.com/paper/2601.01225/) (2026)
- [Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case](https://mlsanity.com/paper/2404.14977/) (2024)
- [Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions](https://mlsanity.com/paper/2412.03600/) (2024)

## Abstract (arXiv)

Style analysis, which is relatively a less explored topic, enables several interesting applications. For instance, it allows authors to adjust their writing style to produce a more coherent document in collaboration. Similarly, style analysis can also be used for document provenance and authentication as a primary step. In this paper, we propose an ensemble-based text-processing framework for the classification of single and multi-authored documents, which is one of the key tasks in style analysis. The proposed framework incorporates several state-of-the-art text classification algorithms including classical Machine Learning (ML) algorithms, transformers, and deep learning algorithms both individually and in merit-based late fusion. For the merit-based late fusion, we employed several weight optimization and selection methods to assign merit-based weights to the individual text classification algorithms. We also analyze the impact of the characters on the task that are usually excluded in NLP applications during pre-processing by conducting experiments on both clean and un-clean data. The proposed framework is evaluated on a large-scale benchmark dataset, significantly improving performance over the existing solutions.