TrueFalse Daisy

Context & Motivation

Twenty years ago, a proposal to put a phone in every pocket and let a handful of entities decide much of what humanity sees through social media recommendations would likely have been rejected as a national-security risk. Newspapers face such limits. In 2024 the United Kingdom moved to bar foreign states from owning its newspapers [A]. Phones, by contrast, arrived harmlessly as tools that helped people, so no one restricted them. Gradually however, in most countries, social media and video networks have now overtaken even TV as a source of news [B], making them today's powerful social influence machines.

With AI's data aggregation and reasoning capabilities at scale, communities are increasingly concerned about how much AI recommendation systems shape public debate. Romania's constitutional court annulled the 2024 presidential election after intelligence evidence of interference on one platform. The European Commission opened formal proceedings, including over the platform's recommendation system [C]. Around Bangladesh's 2026 election, one review documented 72 cases of AI-manipulated content [D].

Yet no study has measured whether recommendation systems steer an entire population in the same direction. One controlled experiment showed that a model given only basic facts about a person was more persuasive than a human in a debate [E]. [DaisyDial] (https://yellowpaperdaisies.org/idea/2026-10-19-telltale-daisy) (yesterday's idea) asks how we can see an AI's stance. TrueFalse Daisy asks a different question: how can humans learn to tell how well they tell true from false on social media?

Target Audience

Anyone who reads the news and uses AI and wants a clear sense of awareness of their own accuracy. Also employers, platforms, and regulators who need a running measure so slow, population level shifts can be spotted early instead of after the fact.

Customer Problems

Companies already know that simply telling staff to “be careful” does not stop phishing. The practical response was to send employees realistic fake phishing emails and then reveal which ones were fake. Each person finally saw their own result and became slightly more aware of the human susceptibility to social engineering.

Misinformation has no equivalent. A voter or reader meets false claims every day and almost never learns which false stories they believed on social media. Without that feedback and awareness in the moment, people cannot calibrate how well they tell true from false. Under political or social strain, a community can be moved in ways that stay invisible and unmeasured.

Hypothesis

1. No feedback: People rarely learn which false claims they accepted, so they cannot judge their own accuracy.
2. Overconfidence: Most people believe others are more easily swayed than they are.
3. Tipping points: Societies under strain may be easier to move with targeted influence.
4. Doubting everything: Misinformation also causes people to dismiss real news as false news.
5. No running measure: Existing studies are short, one-off, and rarely repeated under real conditions.

Solutions

1. A reveal test: An optional feature that purposely mixes true and false items that appear in a normal social media feed (similar to phishing tests). Users can swipe up on any claim they believe is misinformation. If they scroll past a false claim without flagging it, the platform itself notifies them that they missed it and shows the accurate version.
2. Long-running studies: Independent teams in multiple countries, including those under strain, run the same core measures and pool results. This creates a continuous signal instead of isolated snapshots.
3. Who pays: Governments could require social media companies to fund the test as a public education measure. Public research bodies could fund studies of the results to improve society's resilience to misinformation.

Outcome

People develop a practiced sense of their own accuracy. Regular, specific feedback turns “how well do I tell true from false?” from an abstract hope into something visible and improvable.

Societies gain an early warning when people’s ability to tell true from false starts to slip. Aggregated results across countries and over time show gradual impacts of misinformation before they affect elections, public behavior, or social cohesion.

The systems that shape attention receive an external check. Platforms, employers, and public bodies can see what measures are improving or degrading users’ measured accuracy.

Longer-term cultural and institutional effects. Visible personal scores reduce the common overconfidence gap (“others are more easily swayed than I am”). Accuracy becomes a normal, trackable trait rather than an unmeasured ideal.

Appendix

1. On the UK ban on foreign state ownership of newspapers, read the news report and a legal summary.
2. Oxford University's annual digital news report, 2025: in the US, social media and video networks (54%) overtook TV news (50%) for the first time. Read a summary of the findings
3. Report on the European Commission's proceedings of 17 December 2024 and the annulled Romanian election, Euronews. Read the report
4. Naeen, Global Voices, 1 April 2026, review of fact-checks from December 2025 to February 2026. Read the article
5. Salvi et al. (2025), Nature Human Behaviour (preprint; published version may differ). Read the preprint