I understand that is alpha and experimental but I think it’s fundamentally flawed. The fundamental problem is that by assigning a concrete influence score to influencers you are modifying the very thing you are trying to observe. You create strong incentives for identities to react in ways to specifically modify and monetize the influencer metric.
The same argument could be made about impact scores that are used to measure the influence of academic papers. Does this mean that you think they should be abandoned?
Already this has a serious issue. Twitter is not a public utility. It is not an open platform, it has no transparent governance, and perversely incentivizes gaining influence over audience. Twitter can silently suppress or even censor certain topics and people. What would prevent Twitter itself from doing this to promote its own agenda for an influence score?
We share these concerns about using Twitter. That’s the reason this is only a starting point and our objective is to start using multiple sources of data as soon as possible.
I’d rather use a decentralized source, but there does not seem to be a better alternative at this point.
The other issue is Twitter is not reflective of greater society. It’s very much loved by mainstream and social media users but is not so popular outside of that.
It’s not used by the “greater society”, but it has a high penetration within the crypto community. And that’s enough to start with.
We believe that this is the first time a score like this can be built. And this is due to the peculiar nature of the crypto ecosystem. It’s the first ‘global first, local second’ ecosystem. This means that significantly more communication happens online within this community, compared to say, a startup ecosystem.
This leads us to believe that it might be possible to collect enough data to assess influence probabilistically.
We believe that crypto will “eat the world”. If this is the case, then the solutions that work in this community will spill over to other sectors.
Additionally, Twitter has serious issues with both bots and extensive use of block-chains (where you automatically block people based on who they follow, not their interactions with you can lead to severely distorted echo chambers that leads to endless tempests in teapots that blow over within 24 hours. I think even Facebook or Bitcoin Talk) would be fairer.
Twitter has many issues. However, the examples you brought up have not been problematic so far in our experience.
I don’t see how Facebook could be a better source of data for this purpose. We don’t exclude the possibility of using BitcoinTalk.
The way you prevent corruption is by removing temptation, not by designating moral guardians.
I think I was not clear in my comment about corruption. We don’t claim to solve this problem.
What I intended to point out was that people with strong reputation have more at stake, therefore should be less likely to accept bribes (or at least the cost of bribery should increase).
If you want to assign influence score, I’d stick to on-chain solutions that are openly verifiable. I do think an influencer score causes more issues than it solves.
I’m not aware of any on-chain solution that solves the problem we’re addressing. I’d love to change my mind on this, so feel free to educate me.
Besides, our goal is to open-source our algorithm and decentralize its governance. We just believe that starting in a centralized model and decentralizing once we prove that it can work increases chances of success.
Besides, influencers already have influence. That’s why they’re influencers! If they want people to vote a certain way, they can influence them to do so and that would be a far more accurate measure of their social influence than any metric we could come up with.
Good point. One can even think of this as a form of “voting with attention”. Many governance models run into the problem that few voters participate. Even with delegated voting. In this case, delegation happens naturally and is predicated on one’s self-interest.
E.g. let’s say there is a group of physicists. Each one of them has limited attention. They can dedicate it to their own thinking or reading each other’s papers. The optimal strategy is to spend some of it on your own work and dedicate some to keeping track of important ideas produced by others.
This is likely going to lead to the following outcome: those regarded as producing the highest quality thinking will accumulate disproportionally more attention than those producing low-quality thinking.
This distribution is predicated on self-interest – i.e. I’m reading papers that I think will benefit my work.
Arguably, the same dynamics applies to other groups. Including open-source developers.
Centralized networks (e.g. corporations, governments, armies), can make quick decisions because it’s clear who holds power*. The problem with decentralized networks is that there is nobody who does.
There are people who have influence, however. There is just no way (yet) of telling who they are and how much influence they have.
Our hypothesis is that if there is a reliable way of quantifying this influence it opens up possibilities for removing this weakness. This would make decentralized networks even more compelling.
*Power is influence with agency.