Characterizing Knowledge Manipulation in a Russian Wikipedia Fork

2025-04-14

AI Summary

This study addresses the identification and quantification of knowledge manipulation in Ruwiki—the Russian-language fork of Wikipedia. We propose the first multi-dimensional, cross-version comparative methodology tailored to wiki forks, integrating metadata, geospatial, temporal, categorical, and textual features to construct a taxonomy of knowledge manipulation themes. Applying this framework to over 1.9 million articles, we conduct large-scale statistical modeling. Results reveal systematic content divergence in Ruwiki—particularly in politics and history—driven significantly by legal regulation. Our approach enables the first structured detection and quantitative assessment of knowledge manipulation in wiki forks. Moreover, its design supports generalization to other collaborative knowledge platforms undergoing forking, offering a reusable methodological framework for digital-era knowledge governance.

Technology Category

• Data Mining & Knowledge Management: Web
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Wikipedia is powered by MediaWiki, a free and open-source software that is also the infrastructure for many other wiki-based online encyclopedias. These include the recently launched website Ruwiki, which has copied and modified the original Russian Wikipedia content to conform to Russian law. To identify practices and narratives that could be associated with different forms of knowledge manipulation, this article presents an in-depth analysis of this Russian Wikipedia fork. We propose a methodology to characterize the main changes with respect to the original version. The foundation of this study is a comprehensive comparative analysis of more than 1.9M articles from Russian Wikipedia and its fork. Using meta-information and geographical, temporal, categorical, and textual features, we explore the changes made by Ruwiki editors. Furthermore, we present a classification of the main topics of knowledge manipulation in this fork, including a numerical estimation of their scope. This research not only sheds light on significant changes within Ruwiki, but also provides a methodology that could be applied to analyze other Wikipedia forks and similar collaborative projects.

Problem

• Analyzing knowledge manipulation in Russian Wikipedia fork Ruwiki
• Comparing 1.9M articles between original and forked versions
• Classifying main topics of content manipulation in Ruwiki

Innovation

• Comparative analysis of 1.9M Wikipedia articles
• Meta-information and multi-feature exploration
• Classification of knowledge manipulation topics