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

TL;DR

This paper broadens the understanding of online manipulation by introducing strategic misrepresentation, a comprehensive framework that encompasses content, actors, processes, and covertness, supported by a survey of detection techniques.

Contribution

It formalizes strategic misrepresentation as a unifying concept and synthesizes detection methods across disciplines for better identification of information campaigns.

Findings

Introduces a four-dimensional framework for strategic misrepresentation.

Synthesizes detection techniques from machine learning, network science, and visual analytics.

Provides a foundation for classifying and evaluating information campaigns.

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.