What is mindshare?

How do you measure mindshare?

Why does someone with the same mindshare show higher on the leaderboard?

We often answer such questions during AMAs and in community chats, but we still see new ones emerging. So, it’s time to answer everything regarding mindshare in one article.

At Wallchain, we define mindshare as the measure of how much meaningful, credible attention, influence, or conversation a person or project can drive to a specific topic on Crypto Twitter. It’s not just about how many people saw your posts about something, but how deeply people genuinely engaged with them.

In short:

This is one of the core concepts at Wallchain. We’re building an AttentionFi ecosystem, where credible attention is defined by who is paying attention, how meaningfully they engage, and whether their engagement drives real conversations or on-chain actions.

Wallchain operationalizes this with Mindshare Leaderboards that measure and reward high-quality, hard-to-game signals across X and, increasingly, onchain behavior.

Imagine that you opened a restaurant and want to reward everyone who tells others about it.

But there are many ways to tell others about your restaurant. So, how can you measure each person’s contribution and reward them accordingly?

Someone could just stand in front of the building and shout for people to come in. This might work as anti-advertising or bring no meaningful results.

Someone else may mention you to their neighbors or acquaintances in a casual conversation or include you in a comment on social media.

Another person might be a restaurant critic or food blogger and write about you for a much larger audience.

In all these cases, we’re dealing with different qualities of attention and potential impact. What is “quality” in this case?

A person shouting in front of a restaurant or leaving random comments under food posts on social media is unlikely to gain credible attention because it looks like spam or low-effort content.

Conversely, a restaurant critic featured in a local magazine or a sincere recommendation to a friend looking for a place to eat can attract the most interested audience.

So we face the question: how do you measure the quality of attention that your users, fans, brand advocates, or promoters bring to your restaurant in each case, and reward them accordingly?

In the restaurant example, you could ask each visitor who recommended your place. It seems simple, but real business is far more complicated.

To start talking about you, your potential contributors need the right motivation. They won’t always be willing to work for you if you only reward them for the visitors who actually have a meal in your restaurant. After all, they put effort into promotion, but not everything depends on them – your restaurant might not handle the influx of guests, the food might not be great, or the waiter might be rude, and people could leave without eating.

In this case, it would be fairer to reward them for the impact they cause. But how do you reward real impact – and, more importantly, how do you measure it?

This is where we come to the concept of mindshare.

To measure impact, we need two things:

Continuing with our restaurant example, let’s imagine that after opening, we have only ten supporters who generate mindshare – the amount of attention we receive as a business. If each of them tells ten friends about us, the total generated mindshare can be estimated at 100 units (where one unit of attention equals one person).

With equal quality of attention, i.e., if each of our fans told equally interested people about us, we can assume that each supporter received 10% of the mindshare that day.

The next day, all of our restaurant’s acquaintances (100 people) might also start talking about it. Among these hundred, there could be a food blogger who tells 110 people about the restaurant through their blog, while the other 99 each tell only ten.

In this example, the total mindshare for the day would be 1,100 units, of which 10% would belong to the food blogger. In percentage terms, this is the same 10% that all participants received on the first day, but in absolute terms, the difference is significant (10 people reached vs. 110).

Also, each of the 99 participants who attracted ten people on the second day would now receive much less mindshare than on the first day.

This example is very important for understanding mindshare. Every day, different numbers of people generate different amounts of mindshare, and 1% yesterday is not equal to 1% today due to differences in absolute numbers. And even with the same result as yesterday, some participants will get less mindshare on the next day because of increased competition and different abilities to generate attention. 

Moreover, there’s also credibility or quality of attention. In our example, we assumed that all people whom we told about the restaurant would go there and order food.

In reality, one participant might tell ten people, and all ten will go and order food. Another might tell 100 people, but only three will actually visit.

Quality of attention also matters because one contributor might attract ten customers who together spend $2,000, while another brings 100 people who only buy coffee for $3 each.

These are the kinds of things to keep in mind when measuring mindshare.

If you’ve followed all the analogies above, you’re now pretty knowledgeable about mindshare, as general logic is pretty the same. The exact Wallchain algorithm is undisclosed, but let’s reveal a few more important things.

In real life, a restaurant’s marketing team might want to work only with critics and food bloggers, or rely solely on direct marketing. They simply don’t want to work with everyone because it’s time-consuming, difficult to track and measure, and hard to reward fairly.

In Web3 influencer marketing, especially in the InfoFi/AttentionFi era, anyone can become a project promoter. The algo collects all the content, evaluates its authenticity, impact, and credibility. Then we add everything up and calculate the numbers. Ranks on leaderboards reflect those units of absolute mindshare you receive, but translated into %. 

Of course, we face challenges like filtering out noise (AI-generated content without even the basic understanding of the content, spam, “bulletin-board” timelines, etc.) and low-effort contributions. That’s why we use X Score and an LLM model for content analysis.

The task of X Score is to measure how well-known you are on Crypto Twitter. Think of it as the ability to tell someone to visit a restaurant – and they listen and act as you said, because they know you’re knowledgeable about food.

The LLM measures content on CT to understand its value – its impact and ability to drive meaningful conversations. We repeatedly emphasize that mindshare ≠ generic engagement. It’s about the meaningful attention you command, who you influence, and how deeply. Not shouting at the doors (getting back to our example). 

We also analyze various signals derived from natural language understanding and on-chain actions. In essence, it comes down to four things:

That’s it.

In our restaurant story, where you are the owner, mindshare is the fair, data-driven way to see who genuinely put your place on the map – and to reward them proportionally.

On Wallchain, projects do the same at a Web3 scale: elevating credible voices, discouraging spam-to-earn behavior, and tying recognition (and often rewards) to contributors who actually move the needle.