S934: Information Color Wheel.

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Categorizing information as misinformation, disinformation, or “right” (accurate) information can be streamlined by adopting established standards and frameworks used in media literacy, journalism, and information science. These standards help you systematically classify information and align with your True/False and Verified/Unverified matrix. Below are some widely recognized standards and frameworks, along with practical ways to apply them.

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Key Definitions for Categorization

Before diving into standards, let’s clarify the terms:

• Misinformation: False or inaccurate information spread unintentionally (e.g., someone sharing a false claim without knowing it’s wrong).
• Disinformation: False information spread deliberately to deceive or manipulate (e.g., fabricated news stories, propaganda).
• Right Information: Accurate, evidence-based information that aligns with verified facts (e.g., peer-reviewed studies, official reports).
• Malinformation: Information that is true but shared with harmful intent, often out of context (e.g., leaking private data to cause harm).

These categories can map onto your matrix:

• Right Information: Verified + True.
• Misinformation: Often Unverified + False (unintentional).
• Disinformation: Often Unverified + False (intentional).
• Malinformation: Could be Verified + True but with harmful intent (requires assessing purpose).

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Standards and Frameworks for Information Categorization

1. UNESCO’s Information Disorder Framework

• Overview: Developed by UNESCO, this framework categorizes information disorders into misinformation, disinformation, and malinformation based on intent and accuracy.
• Criteria:
  • Intent: Was the false information spread deliberately (disinformation) or unintentionally (misinformation)?
  • Accuracy: Is the information factually incorrect (misinformation/disinformation) or true but harmful (malinformation)?
  • Context: Is the information taken out of context to mislead?
• How to Apply:
  • Investigate the source’s intent. For example, a viral post claiming “vaccines cause autism” might be disinformation if the poster knowingly cites debunked studies, or misinformation if they genuinely believe it.
  • Check accuracy against credible sources (e.g., CDC, WHO). If false, it’s either misinformation or disinformation.
  • Assess context. A true statistic shared to incite fear (e.g., “50% of people died” without mentioning the sample size was 2) could be malinformation.
• Matrix Impact:
  • Disinformation: Unverified + False (intentional deception often lacks credible sourcing).
  • Misinformation: Unverified + False (but intent isn’t malicious).
  • Malinformation: Verified + True but with a harmful purpose (evaluate intent using the CRAAP Test’s Purpose criterion).

2. First Draft’s Information Disorder Typology

• Overview: First Draft, a nonprofit focused on combating misinformation, expands on UNESCO’s framework by identifying seven types of mis/disinformation based on content and intent.
• Categories:
  1. Satire/Parody: False but not intended to deceive (e.g., The Onion).
  2. False Connection: Headlines or visuals don’t match the content (e.g., clickbait).
  3. Misleading Content: True information presented in a misleading way (e.g., cherry-picked data).
  4. False Context: True content shared with incorrect context (e.g., an old photo tied to a current event).
  5. Imposter Content: Content pretending to be from a credible source (e.g., fake BBC logo).
  6. Fabricated Content: Completely made-up information (e.g., fake news articles).
  7. Manipulated Content: Altered media (e.g., doctored photos, deepfakes).
• How to Apply:
  • Identify the type of content. For example, a doctored image of a politician (Manipulated Content) is disinformation.
  • Cross-check with original sources. If a headline doesn’t match the article (False Connection), it’s likely misinformation or disinformation.
  • Evaluate intent. Satire isn’t meant to deceive, so it wouldn’t classify as disinformation despite being false.
• Matrix Impact:
  • Fabricated/Manipulated Content: Unverified + False (disinformation).
  • Misleading/False Context: Unverified + False (misinformation or disinformation, depending on intent).
  • Satire: Unverified + False but not harmful if clearly labeled.

3. The News Literacy Project’s Standards

• Overview: The News Literacy Project (NLP) provides guidelines for identifying credible information and distinguishing it from misinformation/disinformation.
• Criteria:
  • Verification: Is the information independently verifiable through primary sources or fact-checking?
  • Transparency: Does the source disclose its methods, sources, and potential biases?
  • Accountability: Does the source correct errors when identified?
  • Intent: Does the source aim to inform, or does it have a persuasive or manipulative agenda?
• How to Apply:
  • Check if the information can be verified. For example, a claim about a new law should link to a government document.
  • Look for transparency. A news outlet that cites its sources and methodology (e.g., “We interviewed 500 people”) is more credible.
  • Assess accountability. If a source has a history of uncorrected errors, it’s less reliable.
  • Evaluate intent using tone and purpose. A neutral report is more likely “right information” than a sensationalized one.
• Matrix Impact:
  • Verified, transparent sources with accountability: Verified + True (right information).
  • Unverifiable, opaque sources with manipulative intent: Unverified + False (disinformation).
  • Sources with errors but no malicious intent: Unverified + False (misinformation).

4. The International Fact-Checking Network (IFCN) Code of Principles

• Overview: The IFCN sets standards for fact-checking organizations, which can be adapted to categorize information.
• Criteria:
  • Nonpartisanship: The source should be free of political or ideological bias.
  • Fairness: The source should present all relevant evidence, not just one side.
  • Evidence-Based: Claims must be backed by primary sources or data.
  • Corrections Policy: The source should have a clear process for correcting errors.
• How to Apply:
  • Assess bias. A source pushing a one-sided narrative (e.g., only highlighting negative vaccine stories) may be spreading disinformation.
  • Look for evidence. A claim without data or citations (e.g., “Crime rates are soaring!”) is likely misinformation or disinformation.
  • Check for corrections. Reputable sources admit and fix mistakes, increasing their likelihood of being “right information.”
• Matrix Impact:
  • Nonpartisan, evidence-based sources: Verified + True (right information).
  • Biased, unevidenced sources: Unverified + False (disinformation or misinformation).

5. The SIFT Method (Stop, Investigate, Find, Trace)

• Overview: Developed by digital literacy expert Mike Caulfield, SIFT is a practical method for evaluating information.
• Steps:
  1. Stop: Don’t react or share until you’ve evaluated the information.
  2. Investigate the Source: Research the author, publisher, and their credibility.
  3. Find Better Coverage: Look for other sources to confirm or refute the claim.
  4. Trace to the Original: Find the primary source of the claim (e.g., the original study, not a blog post about it).
• How to Apply:
  • Stop and investigate a claim like “New study proves X.” Who published it? A reputable journal or a random blog?
  • Find better coverage. If other credible outlets aren’t reporting the same claim, it might be false.
  • Trace the claim. If the “study” doesn’t exist or is misrepresented, it’s likely disinformation.
• Matrix Impact:
  • Claims traced to credible, original sources: Verified + True (right information).
  • Claims with no original source or contradicted by better coverage: Unverified + False (misinformation or disinformation).

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Practical Application to Your Matrix

Let’s categorize an example using these standards:

Claim: “A new virus is spreading, and the government is hiding it.”

1. UNESCO Framework:

• Intent: The post is from an anonymous account with a history of conspiracy theories (likely deliberate—disinformation).
• Accuracy: No evidence; WHO and CDC report no such virus (False).
• Category: Disinformation.
• Matrix: Unverified + False.

1. First Draft Typology:

• Type: Fabricated Content (completely made-up claim).
• Category: Disinformation.
• Matrix: Unverified + False.

1. News Literacy Project:

• Verification: No evidence or primary sources.
• Transparency: Anonymous account, no methodology.
• Intent: Likely to incite fear (manipulative).
• Category: Disinformation.
• Matrix: Unverified + False.

1. IFCN Principles:

• Bias: Conspiracy-driven, not neutral.
• Evidence: None provided.
• Category: Disinformation.
• Matrix: Unverified + False.

1. SIFT Method:

• Investigate: Anonymous account, no credibility.
• Find: No reputable sources report this.
• Trace: No original evidence found.
• Category: Disinformation.
• Matrix: Unverified + False.

Conclusion: The claim is consistently categorized as disinformation and placed in the Unverified + False quadrant.

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Additional Tips

• Use Multiple Standards: Combining frameworks (e.g., UNESCO + SIFT) ensures a more robust evaluation.
• Check Intent with Digital Forensics: Look for signs of manipulation (e.g., reverse image search for fake photos, check posting patterns on X for coordinated disinformation campaigns).
• Leverage Fact-Checking Databases: Use resources like the Duke Reporters’ Lab or the European Digital Media Observatory (EDMO) for pre-vetted categorizations.
• Monitor Patterns: Sources repeatedly spreading disinformation (e.g., fake news sites) should be flagged as unverified in future evaluations.

By applying these standards, you can systematically categorize information as misinformation, disinformation, or right information and accurately place it on your matrix. If you have a specific claim to evaluate, I can help apply these frameworks!