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Main Authors: Malkamäki, Arttu, Balinhas, Daniel, Iannucci, Letizia, Vine, Megan, Temmermans, Frederik, Coppen, Adrien, Deligiannis, Nikos, Kivelä, Mikko, Quayle, Michael, Varol, Onur, McGee, Fintan
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2603.25883
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author Malkamäki, Arttu
Balinhas, Daniel
Iannucci, Letizia
Vine, Megan
Temmermans, Frederik
Coppen, Adrien
Deligiannis, Nikos
Kivelä, Mikko
Quayle, Michael
Varol, Onur
McGee, Fintan
author_facet Malkamäki, Arttu
Balinhas, Daniel
Iannucci, Letizia
Vine, Megan
Temmermans, Frederik
Coppen, Adrien
Deligiannis, Nikos
Kivelä, Mikko
Quayle, Michael
Varol, Onur
McGee, Fintan
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_25883
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Beyond Disinformation: Strategic Misrepresentation across Content, Actors, Processes, and Covertness
Malkamäki, Arttu
Balinhas, Daniel
Iannucci, Letizia
Vine, Megan
Temmermans, Frederik
Coppen, Adrien
Deligiannis, Nikos
Kivelä, Mikko
Quayle, Michael
Varol, Onur
McGee, Fintan
Social and Information Networks
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.
title Beyond Disinformation: Strategic Misrepresentation across Content, Actors, Processes, and Covertness
topic Social and Information Networks
url https://arxiv.org/abs/2603.25883