How People Verify Information When They Are Unsure — 2025–2026 Data

This page summarises findings from multiple Human Clarity Institute datasets, examining how people respond when they are unsure whether information is trustworthy. Across digital environments shaped by AI and uncertainty, the data show a clear behavioural pattern: people increasingly verify information rather than relying on first impressions.

Digital Trust 2025 →
Trust Calibration 2026 →
AI Media & Authenticity 2025 →

Construct tags: Trust Calibration · Behavioural Response · Information Verification

What the data shows

Three signals stand out across these datasets: verification behaviour is near-universal when uncertainty appears, people actively cross-check information rather than relying on single sources, and this behaviour has become a default response rather than an occasional action. Together, these findings suggest that trust online is increasingly managed through active verification rather than passive belief.

How percentages are defined on this page: unless otherwise stated, summary figures combine respondents who selected 5–7 on the 7-point agreement scale (slightly agree, moderately agree, or strongly agree). Figures are drawn from multiple HCI datasets and reflect comparable behavioural measures across studies.
95%

Double-check information when unsure

Almost all respondents check additional sources before deciding what to believe.

88%

Use external sources to verify claims

Most people consult multiple sources when judging whether information is trustworthy.

Overall, the data show that verification has become a default behaviour in digital environments. Rather than relying on first impressions, people increasingly check, compare, and confirm information before trusting it.

By the numbers (from HCI data)

92%

Check behavioural clues when authenticity is unclear

In the AI Media & Online Authenticity 2025 dataset, most respondents report looking for behavioural signs that content may be AI-generated or manipulated.

91%

Check visual clues for signs of AI-generated content

Most respondents look for visual signals when deciding whether online content is genuine.

73%

Feel confident verifying information

In Digital Trust 2025, most respondents report confidence in their ability to verify information when needed.

54%

Verify mainly when information feels important

In Trust Calibration 2026, just over half say they verify information mainly when the stakes feel high or the content matters to them.

14%

Skip verification when it feels too effortful

A smaller minority report sometimes not checking information because verification feels too demanding or time-consuming.

Patterns observed in the data

Verification has become a default response

The strongest pattern across datasets is that people do not rely on first impressions when uncertainty appears. Instead, checking information has become routine behaviour.

People rely on multiple sources rather than single signals

The use of external sources suggests that trust is no longer based on one signal alone, but on comparison across multiple inputs.

Trust is increasingly active, not passive

These findings indicate a shift from passive belief to active verification. People are not simply trusting or distrusting information — they are actively working to confirm it.

Questions this data can answer

Do people double-check information when unsure?

95% double-check information when they are unsure.

How common is verification behaviour online?

87% verify information when they encounter uncertainty.

Do people use multiple sources to check information?

88% use external sources to verify claims.

Do people feel confident verifying information?

73% feel confident in their ability to verify information.

Methodology

This page combines findings from multiple Human Clarity Institute datasets, including Digital Trust 2025, AI Media & Online Authenticity 2025, and Trust Calibration 2026. It focuses specifically on behavioural responses to uncertainty, examining how people verify information when deciding what to trust.

All datasets use cross-sectional survey designs and collect responses from adults across six English-speaking countries. Participants provided explicit consent for anonymised open publication.

The results presented here are descriptive and reflect observed patterns within survey samples. They do not imply causation or represent population-level estimates.