The Social Content Fact-Checking Checklist: A Reliable Pre-Publication Review

Publishing teams often search for how to prevent factual errors on social media before a post goes live. The answer requires moving beyond informal proofreading to establish a repeatable, traceable verification workflow for every claim, statistic, visual asset, and link.

A pre-publication fact-check is a structured review that isolates every assertion in a social post and verifies it against authoritative evidence before distribution. Implementing this review protocol establishes clear accountability, protects brand credibility, and creates an audit trail that makes your publishing operations resilient against misinformation.

Key Takeaways

• Treat every social post as a bundle of distinct factual assertions, including text overlays, audio tracks, captions, alt text, and linked destinations.
• Separate the technical verification of an asset from legal permission, because confirming an image is authentic does not grant copyright authorization to publish it.
• Cross-check primary datasets directly rather than relying on secondary reporting, paying close attention to sample sizes, dates, and percentage calculations.
• Establish clear editorial protocols for unresolved claims, allowing reviewers to narrow language, delay scheduling, or hold content rather than guessing.
• Maintain a lightweight verification record that documents the source, reviewer, and corroborating evidence for every sensitive claim.

Core Principles of Fact-Checking in Social Operations

A social media post is rarely a single statement. A standard carousel or short video often bundles a headline, numerical claims, direct quotations, background imagery, voiceover narration, and an outbound link. Each component can fail independently. A caption can be entirely accurate while an accompanying chart distorts a historical trend. Similarly, a quotation can be authentic while the attribution misidentifies the speaker’s current title.

Verification in content operations must address the difference between claims, evidence, and sources. A claim is any assertion that can be evaluated against reality. Evidence consists of the documentation, data, or eyewitness testimony used to assess that claim. A source is the entity providing that evidence. When teams evaluate content, they must ensure the evidence directly supports the specific wording chosen for the post.

Corroboration requires independent confirmation. Relying on three separate social accounts that all quote the same unverified blog post is not corroboration. True verification traces an assertion back to its origin, such as an official public record, a peer-reviewed dataset, or direct on-the-record testimony. News organizations like the Associated Press and Reuters structure their standards around this principle, requiring direct attribution and document-based confirmation before publishing claims gathered from third parties.

Editorial teams must also distinguish between primary and secondary sources. A primary source is an original document, raw dataset, direct recording, or firsthand witness. A secondary source is an analysis, summary, or report that interprets primary material. While secondary sources can provide helpful context, high-stakes claims require checking the underlying primary record. Doing so ensures that another writer’s misinterpretation does not enter your scheduled queue.

Context and framing represent the final structural requirement. Content can be factually true in its individual parts yet completely misleading in its overall presentation. Omitting the year a study was conducted, removing the denominator from a ratio, or juxtaposing an old photo with current news creates a false impression. A disciplined pre-publication workflow tests whether the post represents the full picture or creates an unsupported conclusion.

The Six-Layer Pre-Publication Verification Framework

To review multi-element posts efficiently, teams should process drafts through six distinct operational layers. This prevents reviewers from becoming overwhelmed by trying to check grammar, copyright, data accuracy, and link mechanics all at once. By following a structured sequence, editors can systematically eliminate risk before content enters the social publishing workspace.

• 1. Extract - Isolate every factual assertion, graphic label, and implied claim
• 2. Source - Identify the origin and authority of the supporting evidence
• 3. Test - Corroborate facts, audit numbers, and inspect visual media
• 4. Authorize - Confirm licensing, usage rights, and individual consent
• 5. Render - Review final layout, links, alt text, and mobile appearance
• 6. Record - Document approvals, caveats, and correction protocols

Layer 1: Extraction

The reviewer reads the draft and extracts every checkable item into an itemized mental or written list. This includes:

• Explicit factual assertions in the body copy.
• Text embedded within images, carousels, or video title cards.
• Spoken statements in audio tracks or video voiceovers.
• Implied claims created by headline phrasing or visual comparisons.
• Alt text descriptions and accessibility tags.

Layer 2: Sourcing

For every extracted element, the reviewer identifies the underlying source. The editor asks who created the information, whether the source has firsthand knowledge, and whether the material represents original research or a summary. If a claim relies on an anonymous social profile or an unlinked blog, the reviewer flags it for primary source confirmation.

Layer 3: Testing

The reviewer evaluates the claim against the source material. This step involves auditing raw figures, checking dates, corroborating statements with independent documentation, and running reverse-image searches on media files. If the evidence only supports a limited claim, the copy must be revised to match the proven scope.

Layer 4: Authorization

The reviewer verifies that the team holds the legal right and necessary consent to publish the material. Having confirmed that a video is authentic does not mean the team has permission to distribute it. This layer audits commercial licenses, creator permissions, and privacy considerations.

Layer 5: Rendering

The reviewer inspects the post in its final published state within the scheduling environment. This check ensures that line breaks have not severed context, links route to the intended live pages, and thumbnails display correctly. It also verifies that accessibility metadata is present and accurate.

Layer 6: Recording

The reviewer documents the verification trail for sensitive or high-risk content. The record notes who verified the claims, what evidence was consulted, and what caveats were identified. This documentation ensures that if a post is challenged after publication, the organization can instantly reference its editorial reasoning.

Claim Decomposition and Evidence Alignment

Decomposing a post into checkable units prevents unverified assumptions from slipping through the editorial process. When writers draft copy, they often blend established facts with interpretation and casual superlatives. The fact-checking process strips away these layers to verify the core assertions.

• CLAIM DECOMPOSITION WORKFLOW
• Raw Draft Copy
• “Our revolutionary tool helped over 10,000 businesses double sales.”
• Deconstructed Claims
• 1. Product functionality - Evaluated as marketing claim
• 2. “Over 10,000” - Requires CRM/database verification
• 3. “Double sales” - Requires performance study documentation
• Evidence Alignment
• Database confirms: 8,450 active accounts
• Survey confirms: 42% saw increases, average change was 28%
• Aligned Published Copy
• “Our platform serves thousands of teams, helping many increase sales.”

Superlatives and definitive statements require rigorous scrutiny. Words like “first,” “largest,” “only,” “record-breaking,” and “unprecedented” are frequent sources of factual errors. An organization might announce it is the first to launch a specific service feature, only to discover a regional competitor launched an identical feature months earlier. When a draft contains a superlative, the reviewer must require documentary proof or soften the phrasing to an accurate, defensible statement.

Causal language represents another common failure point. Social copy frequently conflates correlation with causation. A post asserting that an initiative caused a specific community outcome must be supported by methodological research that controls for external factors. If the source report only notes an observed correlation during a given period, verbs like “caused,” “produced,” or “eliminated” must be replaced with accurate descriptive language like “coincided with” or “was followed by.”

Graphic text and video overlays demand equal attention. Design teams often condense detailed copy to fit visual constraints, inadvertently removing necessary nuances. A caption might correctly state that revenue grew in one specific division, while the accompanying graphic proclaims that company-wide revenue reached historic heights. Editors must review text embedded in visual assets with the same precision applied to main post captions.

When aligning claims with evidence, editors should follow the principle of least assertion. If the available evidence supports a narrow fact, write the narrower sentence. Do not stretch a preliminary finding into an established industry shift. When organizing assets in a structured publishing calendar, marking claims that require narrower language helps maintain consistency across multi-post campaigns.

Sourcing Standards and Corroboration Protocols

A fact-check is only as dependable as the sources behind it. Editorial teams must establish baseline standards for what qualifies as acceptable evidence. Relying on casual web searches or unverified social aggregators exposes an organization to fabricated stories and outdated statistics.

• HIERARCHY OF SOURCE RELIABILITY
• Tier 1: Primary Evidence (Highest Authority)
• Direct filings, government datasets, sworn testimony
• Original research reports with transparent methodology
• Raw eyewitness video directly verified with the original creator
• Tier 2: Established News & Institutional Reporting
• Wire services (AP, Reuters) with explicit sourcing
• Peer-reviewed academic publications and institutional white papers
• Tier 3: Secondary Compilations & Corporate Summaries
• Industry blogs summarizing third-party surveys
• Trade press roundups without direct data links
• Tier 4: Unverified Digital Content (Insufficient on its own)
• Social media reposts, aggregate accounts, anonymous forum claims

When evaluating a source, reviewers should investigate the original author’s expertise and potential institutional bias. Consider whether the author had firsthand access to the event or is interpreting third-party claims. Examine whether the publication provides transparent methodology, links to raw data, and names on-the-record contributors. Sources that obscure where their data originated or present estimates as definitive facts should be rejected.

Corroboration requires establishing independent lines of verification. If three industry publications all publish the exact same market share estimate, an editor must verify whether they conducted independent research or are citing the same commercial survey. If all three trace back to a single white paper with an unverified sample size, the team possesses only one source of evidence, not three.

Wire service practices offer a clear model for social teams handling breaking industry news or current events. Guidelines from the Associated Press emphasize that information gathered from social platforms must be corroborated through official records, verified documentation, or direct contact with named sources. Reuters similarly mandates clear attribution for material not gathered directly by the publishing organization. If your post references external reporting, state clearly which organization conducted the original investigation.

Teams managing high-volume distribution across networks need consistent sourcing rules. When planning a weekly content planning checklist, build time into the schedule for source cross-checking. Rushing content directly from a draft document to a live queue without checking the original citations remains one of the primary causes of avoidable retractions.

Visual, Audio, and User-Generated Asset Verification

Visual media can be misattributed, cropped out of context, or altered. The ease with which digital assets can be republished makes visual verification a mandatory step in any social review process. Teams must verify both the authenticity of the file and the accuracy of the caption describing it.

• VISUAL VERIFICATION DECISION TREE
• Visual Asset Submitted
• 1. Reverse Image Search (Google Lens, TinEye)
• Match found from earlier date? - STOP: Recycled media
• No prior match? - Proceed to Step 2
• 2. Environmental & Metadata Inspection
• Check weather, foliage, road signs, architecture
• Inspect EXIF metadata (device, timestamps, coordinates)
• Does environment match the claimed location and date?
• 3. Origin & Creator Validation
• Contact original creator directly
• Confirm what the creator personally witnessed
• Secure documented licensing and consent

Visual verification begins by locating the earliest accessible version of the media. Using reverse-image search engines such as Google Images or TinEye helps determine if an image has circulated in previous years under different headlines. If a photograph purported to show an event from this morning was actually published three years ago, the claim is instantly disproven.

Environmental inspection provides critical verification clues when reverse searches yield no matches. Reviewers should examine visible landmarks, street signage, license plates, architectural styles, and weather conditions. Comparing these details against satellite mapping tools and historical weather reports can confirm or refute a claimed location and time. For video assets, inspectors should conduct frame-by-frame reviews to spot manipulation artifacts, unnatural lighting cuts, or mismatched audio dubbing.

Metadata analysis can reveal the camera model, exposure settings, timestamp, and GPS coordinates of an original file. However, publishing teams must understand the technical limitations of this approach. Research on digital verification shows that most major social media networks automatically strip EXIF metadata upon upload to protect user privacy. The absence of metadata does not prove a file is fake, and metadata can occasionally be spoofed. Metadata is a supporting clue, not an absolute proof point.

When handling user-generated content or eyewitness video, direct outreach to the creator is essential. Editorial teams should ask the filmer where they were standing, what device they used, what occurred immediately before and after the recording, and whether they are the original owner of the file. Documenting this conversation establishes an audit trail that supports both the authenticity of the media and the organization’s editorial diligence.

Data Integrity, Numerical Precision, and Contextual Framing

Numerical errors damage organizational credibility because numbers appear precise and definitive to audiences. A misplaced decimal point, an ambiguous percentage, or a skewed chart axis can fundamentally mislead readers. Editors must audit every quantitative statement down to the underlying raw data.

A frequent numerical error in social content is confusing a percentage change with a percentage-point change. If a metric increases from 10% to 15%, that represents a 5 percentage-point increase, but a 50% relative increase. Conflating these two concepts distorts the magnitude of the change. Reviewers must verify whether survey findings, financial updates, or scientific summaries describe absolute changes or relative proportions.

• NUMERICAL AUDIT CHECKLIST
• Metric Type: Percentage vs Percentage Point
• Original Value: 10%
• New Value: 15%
• Accurate Description: “Rose 5 percentage points” OR “Grew by 50%”
• Inaccurate Description: “Rose by 5%”
• Context Check: Denominator and Sample Boundaries
• Claim: “80% of users prefer our service.”
• Reality: 80% of respondents in a 20-person internal focus group.
• Required Fix: State the sample size and demographic constraints.
• Timeframe Check: Periodicity Alignment
• Claim: “Annual revenue reached record highs.”
• Reality: Q3 performance annualized, not audited full-year totals.
• Required Fix: Specify the exact quarter and measurement methodology.

Contextual integrity is just as critical as numerical accuracy. Presenting a factual statistic without its time frame, geography, or sample population can deceive the reader. Stating that an organization served 50,000 community members sounds impressive, but if that total represents an aggregate accumulated over twenty years rather than the past quarter, the caption must specify the full time range.

Visual representations of data require strict design guardrails. Truncating the vertical axis of a bar chart can make a tiny variation appear like a massive surge. Similarly, plotting non-linear scales without clear labels distorts trends. When publishing data-rich graphics, editors should compare the visual curve directly against the data table to ensure the visual weight matches the underlying reality.

Time-sensitive claims also require explicit publication checks. Words like “currently,” “latest,” “recently,” and “today” quickly become inaccurate if a scheduled post is delayed. When loading evergreen content or scheduling posts across time zones, editors should use explicit dates and time markers rather than relative terms. Checking performance metrics alongside contextual performance data helps teams evaluate past publications with accurate benchmarks.

Rights, Licensing, and Permissions Management

Fact-checking is incomplete without verifying legal rights and permissions. An asset may be genuine, accurately described, and perfectly timed, but publishing it without appropriate authorization creates severe operational and legal risks. Content teams must separate the question of truth from the question of permission.

• PERMISSION VS VERIFICATION MATRIX
• Is it verified? Is it authorized? Publishing Decision
• Yes Yes Approved for publication
• Yes No HOLD: Secure license or remove
• No Yes HOLD: Confirm facts before use
• No No REJECT: Inaccurate and unapproved

Public availability on the internet does not constitute a public license. Downloading a high-resolution photo from a search engine or taking a screenshot of a user’s post does not grant commercial redistribution rights. Official UK government guidance on copyright notices clarifies that copying digital images or sharing them online generally requires explicit authorization from the copyright holder.

Attribution is not a substitute for permission. Writing “credit to original owner” in a caption provides zero legal protection if the copyright holder has not granted a license. Licensing terms must explicitly cover the intended medium, geographic distribution, commercial scope, and duration of the campaign. If your organization plans to run an asset as a paid advertisement, standard editorial or non-commercial permissions will not suffice.

User-generated content campaigns require documented consent workflows. When sourcing community photos or video clips, secure written permission through verified communication channels. Keep records of the interaction, including the account name, date, exact asset link, and the specific terms agreed upon. Major newsrooms maintain formal UGC permissions records that track clear terms of use; brand teams should follow the same standard.

Privacy and consent considerations extend beyond copyright ownership. Publishing images of identifiable private individuals, particularly in sensitive environments or healthcare settings, often requires a signed model release. If a post features vulnerable populations or minors, standard copyright permission from the photographer is insufficient; explicit legal consent from the subjects or their guardians is mandatory.

Accessibility, Link Integrity, and Platform Variations

The final technical step in fact-checking audits the post’s functional components across every target network. Content can become factually inaccurate during the mechanical transfer between drafting tools, asset drives, and publishing platforms. Reviewing the rendered preview prevents display glitches from undermining factual accuracy.

• TECHNICAL RENDERING CHECKPOINTS
• Checkpoint 1: Outbound Link Integrity
• Destination URL opens directly without redirects or 404 errors
• Landing page content matches and supports the specific caption claim
• Descriptive link text is used instead of ambiguous “click here” tags
• Checkpoint 2: Accessibility and Metadata Alignment
• Alt text accurately describes imagery without introducing new claims
• Closed captions match spoken audio word-for-word
• Essential data in infographics is reflected in readable body text
• Checkpoint 3: Multi-Platform Formatting Fidelity
• Character limits have not truncated necessary contextual caveats
• Line breaks preserve the relationship between headers and data points
• Video aspect ratios do not crop out critical on-screen source notes

Outbound links must be tested directly from the final preview environment. Reviewers must click every link to confirm that it resolves to the exact supporting source rather than a generic home page. Furthermore, verify that the linked page actually contains the cited facts. If a landing page changes its copy or removes a dataset after your post is scheduled, your post’s factual backing disappears.

Accessibility standards require rigorous metadata fact-checking. Guidelines from the World Wide Web Consortium (W3C) emphasize that link text should clearly describe the destination’s purpose, advising creators to avoid ambiguous wording such as “click here” or “read more.” Alt text for screen readers must describe visual elements accurately without introducing unverified assumptions. If an infographic presents a key statistical finding, that data must be accessible in text format for users who cannot view the image.

Editorial teams operating multi-account management workflows must check platform-specific variations. A post formatted for LinkedIn may include a complete source citation, while an automated cross-post to another channel might truncate the text before the citation appears. Aspect ratio differences between vertical video platforms and landscape feeds can crop out lower-third source graphics or methodology disclaimers. Reviewing each platform’s rendered layout independently ensures that factual qualifications remain visible everywhere.

Common Verification Pitfalls and Systemic Misconceptions

Understanding why pre-publication checks fail helps teams build stronger editorial safeguards. Most publishing errors do not stem from malice; they result from cognitive shortcuts, time pressures, and unchecked assumptions about digital information.

• COMMON VERIFICATION PITFALLS
• Pitfall: The Circulation Trap
• Flawed assumption: “Widely shared content must be true.”
• Operational reality: Virality reflects engagement, not accuracy.
• Pitfall: The Verification Badge Fallacy
• Flawed assumption: “Verified accounts represent authoritative facts.”
• Operational reality: Badges indicate account status, not truth.
• Pitfall: The Link Illusion
• Flawed assumption: “The presence of a citation link proves accuracy.”
• Operational reality: Links frequently point to irrelevant summaries.
• Pitfall: The Metadata Myth
• Flawed assumption: “Missing metadata proves an asset is fabricated.”
• Operational reality: Social networks strip metadata by default.

The Circulation Trap

A widespread misconception is that an asset must be accurate if numerous high-profile accounts have shared it. Virality measures emotional resonance and algorithmic reach, not factual accuracy. Information cascades happen quickly online, with hundreds of accounts repeating an initial unverified claim within minutes. Content teams must treat recirculated material with the same skepticism applied to unvetted tips.

The Verification Badge Fallacy

Display names, brand logos, and platform subscription badges can easily be mistaken for institutional authority. Today, verification marks on many platforms indicate identity confirmation or paid subscription tiers rather than journalistic credibility. A checkmark does not guarantee that the account holder possesses subject-matter expertise or firsthand knowledge of an event.

The Link Illusion

Placing a hyperlink in a post gives the appearance of thorough research, but readers and reviewers often fail to click through to inspect the destination. Writers sometimes paste a link to an extensive government report, assuming the document supports their headline, when in reality the report reaches a far more qualified conclusion. Every link must be audited to verify that the destination text directly matches the specific assertion.

Behavioral Research on Corrections and Warnings

When errors occur, publishing teams often assume that issuing a quick correction or adding a link resolves the issue. However, behavioral research reveals nuanced audience responses:

• Factual Belief Adjustments: A multi-country experimental study published in the Proceedings of the National Academy of Sciences (PNAS) examined fact-checking across Argentina, Nigeria, South Africa, and the United Kingdom. The researchers found that exposure to fact-checks increased factual belief accuracy by an average of 0.59 points on a five-point scale. While this demonstrates that corrections can improve understanding, it also shows that corrections rarely shift beliefs completely.
• Limitations of User Corrections: A 2025 study published in the Harvard Kennedy School Misinformation Review analyzed short user-generated corrections across Brazil, India, and the United Kingdom. The study found small overall effects on audience beliefs and noted that appending a fact-check link did not significantly increase the effectiveness of short user corrections.
• Downstream Trust Costs: Research published in Social Media + Society investigated the broader effects of political corrections. While corrections improved belief accuracy regarding specific facts, they also led to lower trust in news media and increased perceptions of media bias among users whose prior beliefs were contradicted.
• Engagement Reductions from Platform Flags: An empirical study by Yale University researchers evaluated the impact of algorithmic and community warning flags on social platforms. The study reported that attaching warning notes reduced reposts by an average of 46.1%, likes by 44.1%, replies by 21.9%, and overall views by 13.5%. These flags limit distribution, but they operate primarily as friction against spread rather than a universal guarantee of audience persuasion.
• Correction Framing Effects: A four-country study published in Scientific Reports analyzed how the phrasing of fact-checks impacts reader engagement. In an experimental condition in Argentina, framing a fact-check as a confirmation rather than a direct contradiction increased the user engagement rate from 18.9% to 37.1%, representing an 18.2 percentage-point increase.

These findings emphasize why pre-publication prevention is vastly superior to post-publication corrections. Corrections require careful phrasing, demand significant operational effort, and can generate mixed reactions among audiences. Preventing errors through a pre-publication checklist protects brand credibility far more effectively than issuing retractions.

Traceability Systems and Audit Record Templates

A dependable verification system requires accountability. If an organization cannot identify who checked a claim, what source was consulted, and who authorized publication, the verification process remains fragile. Building a lightweight logging system ensures that quality standards are enforced consistently across the entire team.

• PRE-PUBLICATION AUDIT LOG STRUCTURE
• 1. Asset / Post ID: Internal reference code or calendar link
• 2. Specific Claim: The exact sentence, metric, or visual assertion
• 3. Source Citation: Document name, raw file URL, and access date
• 4. Source Tier: Primary record, institutional report, or direct witness
• 5. Review Method: Dataset audit, reverse-image search, creator contact
• 6. Status: Verified, Qualified, Unresolved, or Rejected
• 7. Rights & Permissions: License ID, written release, or public domain
• 8. Reviewer Name: Responsible editor conducting the verification
• 9. Approver Name: Final publishing authority
• 10. Correction Plan: Action path if data is challenged or updated

Managing Unresolved Claims

Not every claim can be verified before a publication deadline. Editorial teams must establish explicit protocols for handling uncertainty. Reviewers should never feel pressured to make a binary true or false judgment when evidence is ambiguous.

When a claim cannot be verified, teams should apply one of four standardized actions:

• Narrow the Phrasing: Modify the draft to describe only what can be proven with absolute certainty.
• Add Explicit Caveats: Clearly state the limitations of the data directly within the post copy.
• Hold the Content: Delay scheduling until primary sources can be reached and corroboration is complete.
• Remove the Assertion: Cut the unverified claim entirely while keeping the remainder of the post.

Illustrative Editorial Models

To understand how this framework functions in daily operations, consider two model publishing scenarios:

• MODEL A: CORPORATE SUSTAINABILITY CAROUSEL

• Initial Draft Claim

• “Our clean energy transition eliminated 50% of supply chain emissions.”

• Verification Audit

• Primary data check shows a 50% reduction in Scope 1 emissions only.

• Scope 2 and Scope 3 supply chain emissions decreased by 12%.

• The 50% figure was an annualized projection from one facility.

• Audit Outcome & Revision

• Language narrowed to prevent inaccurate environmental claims.

• Corrected Post: “We reduced operational Scope 1 emissions by 50% at

• our primary plant, while total supply chain emissions fell 12%.”

• MODEL B: RAPID-RESPONSE BREAKING INDUSTRY UPDATE

• Initial Draft Claim

• “Regulators just passed sweeping new compliance rules effective today.”

• Verification Audit

• Government agency published a proposed rulemaking notice, not a rule.

• Public comment period remains open for 60 days.

• Secondary blog post misidentified the draft notice as a final law.

• Audit Outcome & Revision

• Fact-check identified secondary source misinterpretation.

• Corrected Post: “Regulators have proposed new compliance rules. The

• draft is now open for a 60-day public comment period.”

These models illustrate that fact-checking is not merely about catching grammatical errors or fabricated stories. It is a systematic process of ensuring that every claim matches the exact scope of the underlying evidence.

Next Steps

To implement a reliable fact-checking workflow across your organization, complete these steps this week:

• Step 1: Audit Upcoming Scheduled Copy Review the next ten posts in your publishing queue. Deconstruct each post into individual factual assertions and verify that a primary source supports every claim.

• Step 2: Establish Source Standards Distribute a clear sourcing policy to your content team. Mandate that numerical data and direct quotations must trace back to primary documentation or on-the-record statements.

• Step 3: Implement Visual Search Protocols Train editors to run reverse-image searches on all third-party and user-submitted visual media before scheduling.

• Step 4: Standardize Permission Documentation Create a central repository to store image licenses, creator message logs, and signed model releases alongside scheduled assets.

• Step 5: Adopt a Verification Audit Log Integrate a simple review log into your editorial review process. Ensure that high-risk content receives documented approval from a designated fact-checker before publication.

Sources

1. Studying the Downstream Effects of Fact-Checking on Social Media: Experiments on Correction Formats, Belief Accuracy, and Media Trust
2. Flagging misinformation on social media reduces engagement …
3. Exposure to social engagement metrics increases vulnerability to …
4. Studying the Downstream Effects of Fact-Checking on Social Media: Experiments on Correction Formats, Belief Accuracy, and Media Trust - Ingrid Bachmann, Sebastián Valenzuela, 2023
5. Combating Misinformation by Sharing the Truth: a Study on …
6. Framing fact-checks as a “confirmation” increases engagement with corrections of misinformation: a four-country study - Scientific Reports
7. How ARD, BBC and CNN verify social media content - Social Media - English - DW.COM
8. Reporting from the Internet and Using Social Media
9. Copyright notice: digital images, photographs and the …
10. 1