Unveiling Deception: Establishing a Taxonomic Framework for Disinformation within Scientific Discourse

TL;DR

This paper presents a comprehensive taxonomy framework for classifying and understanding disinformation in scientific discourse, aiding journalists and policymakers in recognizing and countering such misinformation.

Contribution

It introduces a novel, structured taxonomy for scientific disinformation, developed through literature review, expert analysis, and case studies, filling a gap in existing detection methods.

Findings

Taxonomy effectively categorizes disinformation strategies and motives.

Validated through analysis of 12 real-world disinformation cases.

Provides a tool for better recognition and response to scientific disinformation.

Abstract

Disinformation spreads among the public and in scientific discourse through the actions of individuals, organizations, and governments that distort scholarly communications, media narratives, and institutional trust. This taxonomy introduces a structured framework and specialized set of definitions to elucidate the key participants, platforms, and strategies employed in the propagation of disinformation. Enhanced comprehension of the mechanisms and pathways of scientific disinformation equips journalists and policymakers with the tools necessary to more effectively recognize and address these issues. The authors developed this taxonomy of disinformation through a multi-faceted approach, encompassing a literature review, expert review, and case study analysis. The literature review revealed a scarcity of taxonomical models amidst prevalent algorithmic detection studies. Subsequently, an expert review process refined our taxonomy through collaborative analysis of twenty-two cases of identified disinformation, categorized by their methods, motives, and impacts. Finally, we validated and fine-tuned our taxonomy through detailed case studies of twelve diverse disinformation instances, assessing the taxonomy's effectiveness in capturing the essential characteristics of each case and making necessary adjustments to ensure its relevance and accuracy in real-world applications.