Table of contents
- What newcomer problems stem from high inequality?
- How can you mitigate status concentration and encourage status mobility?
- Accidentally causing status issues
- Further reading
As more people in crypto explore social, I find myself often talking about what makes social networks work and, sometimes, not work. One key conversation is around status. Here are some lessons I learned from my web2 days.
Social networks tend to elevate content that it expects will get attention. Doing so incentivizes a particular type of behavior that lends status to the users performing said behavior. Typically, there’s a status indicator that people have to try and accumulate. It comes in various forms – karma, follower/like counts, XP, the verified badge, leaderboards, etc.
Naive implementations of the above often result in a fatal flaw – concentrating status in a few “status rich” users leaving the vast “status poor” and ensuring newcomers have a bad experience. While doing so might be value maximizing in the short run, in the long run it’s a bad strategy as new users can’t break in and eventually overall network quality decays.
First, how do you model status in a network? The Gini coefficient is typically a measure of wealth inequality: the more the inequality, the higher the number. For social networks, we can use this as a measure of relative status distribution and use your network’s indicator of status (followers/karma/etc.) as wealth.
In other words: do a small set of your users have outsized status?
This brings me to some personal beliefs on social network design and how social network builders should think of themselves more as modeling economic policy.
The simplest way to think of this is status=capital. You want capital to move around and seek out healthy behavior and not be locked away or used at odds with healthy behavior.
1. Newcomers mimic unhealthy behavior: Your highest status users have figured out how to play the status game – they know how to get millions of followers/answer questions with the most karma/ do the actions that grant them status. However, that behavior may not be what you want your newest users to model themselves on. The natural mimesis that occurs in social networks will then work against you.
Let’s take a current Twitter example: you might have noticed how a lot of tweets now are just threads (how many times have you seen “a 1/37…?) . While that may be the way for someone to get their millionth follower, it definitely isn’t what you want your new users to try doing.
2. People don’t want to play unwinnable games: When a new user shows up in a social network and once they figure out the basic mechanics, they’re going to accumulate some initial status: their first followers, their first karma, their first points. They will then look up the global leaderboard or see how many followers their favorite celebrities have or worse, their peers have. And if they see someone with 100 gazillion karma and they have no means to get close, they will get disheartened, bounce and use something else easier.
Social networks have an acute version of this when you have to produce content – no one wants to post a video/text/photo and have it publicly get no reactions compared to what the norm is.
It’s human nature to try and figure out how to play/win status games and if your users perceive your social network as too hard to play or already won by a certain set of people, they will move onto another game.
3. Status NIMBYism: When you get a certain group with high status, it is common for them to try and keep out newcomers from attaining status.
You can see this often when there is a protest from current users familiar with a certain network “meta” who don’t like change. Without high status mobility, you’ll often get groups with high status that will work together to keep out newcomers.
There are too many examples to count and all variations of September 1993. Remember when Instagram users protested the app launching on Android? Or more recently, Instagram shifting focus to short form video from photos. These will continue to occur as the means to gather status change.
1. “Universal Basic Status”: A common mechanism is to give temporary status boosts to newcomers. This is typically done through algorithmic levers that control distribution and rewards.
You might notice this if you sign up for a new account on any popular social platform. Your content will get recommended more and you’ll get elevated more in friend suggestions, an effect that will decay over time.
There are multiple ways to build these mechanisms into your network.
Note: status has to have inbuilt notions of scarcity for it to matter. If you’re distributing status, you’re causing inflation and might accidentally cause your status signals to be devalued. You can’t “print” new status without side-effects!
2. Make status obscure: Another mitigation is to downplay all indicators of status and make people seek it out. By making status obscure, you give yourself more options to have people focus on the actual game/app mechanic and less on the status mechanic.
You see examples of this direction in recent years. Instagram trying to hide the number of people who liked a post, TikTok downplaying the follower count. All of these status obscuring changes help alleviate this effect among other reasons for them to exist. The downside of this approach is if your network is about status, without indicators people may not know what “game” they’re playing.
3. Set up cohorts of people with similar status levels: If you play any mainstream competitive game, you’ll be familiar with the concept of “ranked” (typically ELO rated) games where the game tries to set you up with people of similar skill levels so you’re more likely to have a challenging but not impossible experience. Similarly, dating apps often try to bucket people of similar “desirability” in an ELO-esque mechanism.
For a network, one way to make for a good newcomer experience is to have a “ranked” experience where they are exposed to or interact with a subset of the entire graph. For example, a sub-reddit instead of everyone competing with everyone else on Reddit.
4. Reset or decay status indicators: One aggressive measure to battle status concentrations is to have every status indicator decay over time – a deflationary measure for your status indicators.
For example, karma that decays the more you stay away from the network or losing followers over time (especially if you have gained a lot of followers from being on an early suggested user list).
To my knowledge, no one has really tried the logical extreme version of this: set all status indicators to zero periodically and reset the network from scratch. Might be an interesting experiment to run!
5. Reset the ‘meta’: One reason the Instagram and Youtube moves to short form video cause controversy is that they “reset the meta” – a concept familiar to gamers anywhere. Doing so in combination with one of the mechanisms above shakes up mobility and changes who can gain status in your network.
Social networks often accidentally run into status issues that are hard to unwind.
The biggest mistake you can make – as astutely pointed out by Eugene Wei – is to not acknowledge how social networks have social capital at their core. Understanding how that capital is created, traded, and consumed is going to make or break your network. Doing so might mean thinking of your role more as a policymaker/economist than a traditional product builder/engineer.
This post was influenced by multiple people.
Eugene Wei – Status as a Service. In my mind, the single best piece of writing on social networks in the last decade and the piece that has influenced me the most. Required reading. This post tries to build on Eugene’s ideas.
James Scott – Seeing Like a State. While the book focuses on historical policies by misguided governments and others, it is actually highly relevant to the management of social networks – and the mistakes they may make when attempting to make things “legible”.
Thi. Nguyen – How Twitter gamifies communication. A fantastic breakdown of the “game” of Twitter. One I wish I had read when I was still working there.
Shout outs to many who read and provided feedback on this – especially Dan Romero, Antonio García Martínez, and Scott Kominers.
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