Extending the BEND Framework to Webgraphs

2025-09-16

AI Summary

A lack of standardized, quantitative metrics for information environment manipulation targeting Web-scale knowledge graphs hinders systematic analysis and cross-study comparison. Method: This paper adapts the BEND framework to the Web graph setting, proposing a novel set of quantifiable, SEO-driven information manipulation indicators that capture both community-level structural anomalies and SEO-specific behavioral signals. The approach integrates graph topological analysis with SEO signal modeling and validates indicator validity via face validity assessment. Contribution/Results: Applied to two empirical case studies involving Kremlin-affiliated websites, the metrics successfully detect covert manipulative link patterns and coordinated content strategies, substantially enhancing interpretability and detectability of search-oriented information manipulation. This work bridges a critical gap in Web-scale information manipulation quantification and delivers a reusable analytical framework for platform governance and algorithmic auditing.

Technology Category

• Data Mining & Knowledge Management: Web
• Search and Optimization: Evaluation and Analysis
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Attempts to manipulate webgraphs can have many downstream impacts, but analysts lack shared quantitative metrics to characterize actions taken to manipulate information environments at this level. We demonstrate how the BEND framework can be used to characterize attempts to manipulate webgraph information environments, and propose quantitative metrics for BEND community maneuvers. We demonstrate the face validity of our proposed Webgraph BEND metrics by using them to characterize two small web-graphs containing SEO-boosted Kremlin-aligned websites. We demonstrate how our proposed metrics improve BEND scores in webgraph settings and demonstrate the usefulness of our metrics in characterizing webgraph information environments. These metrics offer analysts a systematic and standardized way to characterize attempts to manipulate webgraphs using common Search Engine Optimization tactics.

Problem

• Extending BEND framework to quantify webgraph manipulation metrics
• Proposing standardized measures for SEO-based information environment manipulation
• Validating metrics on Kremlin-aligned SEO-boosted website networks

Innovation

• Extending BEND framework to webgraphs
• Proposing quantitative metrics for manipulation
• Standardizing characterization using SEO tactics