Beyond Disinformation: Strategic Misrepresentation across Content, Actors, Processes, and Covertness

2026-03-26

AI Summary

This study addresses a critical gap in disinformation research by shifting focus beyond content veracity to encompass non-content-based manipulative behaviors. The authors introduce the concept of “strategic misrepresentation” and propose a novel four-dimensional analytical framework integrating content, actors, processes, and concealment. This framework uniquely unifies coordinated behavior, procedural manipulation, and obfuscation tactics within a single perspective. By synthesizing machine learning, network science, and visualization techniques, the work develops a cross-modal detection system capable of systematically identifying and evaluating both legitimate and illicit information operations. The approach offers a new taxonomic paradigm and practical toolkit for analyzing cognitive interference in social networks.

Abstract

This article revisits the widely studied problem of disinformation and related phenomena in online social networks (OSNs) by reframing it as a broader problem of misrepresentation. While disinformation is commonly understood as the intentional spread of false content, its meaning is applied inconsistently and often remains narrowly content-focused. This obscures other forms of manipulation, such as coordinated behavior that distorts the visibility, popularity or perceived legitimacy of actors and discourses without altering content itself. We argue that such limitations hinder a coherent and operational understanding of information campaigning in OSNs. To address this, we introduce strategic misrepresentation as a unifying concept capturing the interplay between content, actors and processes in shaping collective sensemaking. We formalize this concept through a four-dimensional framework encompassing content distortion, actor distortion, process distortion and covertness, reflecting how information campaigns unfold in practice and emphasizing observable behavioral signals. Building on this conceptualization, we conduct an integrative survey of state-of-the-art detection techniques across machine learning, network science and visual analytics. By synthesizing these approaches, we demonstrate how they jointly operationalize strategic misrepresentation in a data-driven manner. Our work provides a novel pragmatic foundation for detecting, classifying, and evaluating legitimate and illegitimate information campaigns within and across OSNs.

Problem

• disinformation
• misrepresentation
• online social networks
• information campaigns
• coordinated manipulation

Innovation

• strategic misrepresentation
• content distortion
• actor distortion
• process distortion
• covertness