Defining Misinformation and Understanding its Bounded Nature: Using Expertise and Evidence for Describing Misinformation

Contributions

• Surveys competing definitions of misinformation in the literature, distinguishing between misinformation (the information itself) and misperceptions (beliefs people hold)
• Proposes defining misinformation relative to "clear evidence and expert opinion" rather than merely "inaccurate beliefs"
• Identifies the inherently bounded and time-dependent nature of misinformation definitions
• Catalogs four major challenges: conflicting evidentiary scenarios, expert-consensus thresholds, emerging issues with evolving science, and contradictory or speculative evidence
• Advocates for methodological transparency regarding the temporal state of evidence and expertise at time of study

Method

This is a conceptual paper providing a critical review of how misinformation has been defined across the literature. The authors examine definitions from foundational work (Kuklinski et al., Nyhan & Reifler) and develop a framework based on two criteria:

1. Expertise criteria: level of expert consensus, clarity of who counts as an expert, perceived bias of experts
2. Evidence criteria: amount of evidence available, whether evidence is concrete and observable, universality vs. conditionality of findings

The paper situates issues along a spectrum from settled (clear consensus, abundant concrete evidence) through emerging (evolving science) to controversial (contested expertise or evidence).

Results

The paper does not report empirical findings but rather identifies foundational challenges:

• Expert consensus vs. evidence: For health and science issues, expert consensus can clarify boundaries, but for political issues, defining expertise is harder. Some domains lack expert consensus altogether.
• Defining "adequate" consensus: A 68% agreement threshold (e.g., pesticide safety) differs substantially from near-universal consensus, yet the literature conflates them.
• Emerging issues: In domains like Zika virus transmission or vaping, evidence and expert guidance evolve in real time—what counts as misinformation in January 2016 may be accurate by February 2016.
• Contradictory evidence: When new single studies contradict prior consensus (e.g., fluoridation and IQ), or when evidence is speculative (sunscreen absorption), defining misinformation becomes muddied.
• Political domains: Issues with incentives for particular viewpoints (climate, economic stimulus, election interference) lack clear scientific consensus and invite motivated reasoning from both experts and media.

The paper presents a pyramid model (Figure 1) showing the hierarchy from settled (clear boundaries) to controversial (fuzzy boundaries).

Notes

Strengths: This paper articulates a previously underexamined problem in the field—that misinformation definitions are time- and context-dependent. The expertise/evidence framework is practical and generalizable. The concrete examples (Russia–Trump interference, Zika, sunscreen, fluoridation, pesticides) ground the abstract discussion.

Limitations: As a conceptual piece, it raises problems without proposing concrete operationalizations. Researchers will still face ambiguity when deciding how to classify borderline cases. The paper acknowledges this friction but leaves solutions to downstream work.

Follow-up questions: How should longitudinal studies handle cases where misinformation at time t becomes accepted at time t+1? Should we distinguish between "misinformed at the time" and "wrong in hindsight"? For emerging issues, what degree of evidentiary change warrants reclassification?