This might be a good use-case for the "bayesian truth serum" http://economics.mit.edu/files/1966

This applies when our questions are not just trying to learn about the world (e.g. 'Our survey discovered that 10% of posts are considered misleading'); we are going to use their answers to decide on actions, e.g. removing posts, attaching warning labels, etc.

Those answering the questions know this, and (if they have a preference over which action will be taken) are incentivised to give more extreme answers. A classic example is an ice cream company surveying shoppers about the flavours they like: if I truthfully answer that I like chocolate slightly more than strawberry, this will have a small effect on the survey result, and hence the company's new product flavours. However, if I falsely say that chocolate is the best flavour I've ever encountered, and that strawberry makes me vomit, that will have a much stronger effect on the survey result, and make it more likely that the company will make the chocolate ice cream that I prefer.

The "bayesian truth serum" counteracts this by asking each question in two parts: there's the initial question we want answered, as well as an additional question: "how do you think others will answer?". For example:

- "I find this misleading" and "I think 80% of respondents will find this misleading"

- "I rate strawberry as 4/5" and "I think 10% of respondents will give strawberry 1/5; 20% 2/5; 50% 3/5; 15% 4/5; and 5% 5/5"

The first answers (the ones we care about) are weighted based on two conditions: how closely the estimated distribution matched the real answers, and how 'surprisingly popular' the first answer is.

To see why this cancels-out the incentives to lie: our best chance of affecting the result is to choose a 'surprisingly popular' answer, since this will contribute more weight to the result. However, these two constraints exactly cancel out:

- The answers we predict are popular, will also be those we predict are unsurprising (after all, we could predict them!)

- The answers we predict will be surprising, will also be those we predict are unpopular (that's why it would be surprising if they were popular!)

It turns out that the rational strategy, for swaying decisions as much as possible towards the outcomes we want, is to answer the first part truthfully.

A similar analysis applies to answering the second question (the estimates) truthfully. In that case there are two things to consider:

- We want our estimates to be as close as possible to the true distribution, in order to maximise our response's weight.

- We want to engineer our estimates such that the answers we disagree with get a high estimate, and hence appear 'unsurprising' (reducing the weight of those responses). Our estimates must sum to 100%, so decreasing the 'surprisingness' of one answer must increase the 'surprisingness' of the others. The effect we have on each answer's weight will be small, but it will affect every response which chooses that answer. Hence to have the largest impact, we need to decrease the 'surprisingness' of those answers we think will get the most responses. Yet that exactly what we've been asked for (an estimate of how popular we think each answer will be!)