How to make platform comparisons that hold up

A feature grid is the least useful way to compare social platforms. Features converge, anything popular is copied within a season or two, so a grid captures a moment and then quietly becomes wrong while continuing to look authoritative.

The differences that persist are structural. There are six of them, they change slowly, and once you can read them you can place any platform, including one you have never used, in about ten minutes.

What to take away

The six axes

1. Who chooses what you see. The most consequential axis. At one end, you see what you subscribed to, in the order it arrived. At the other, a recommendation system chooses from everything available, and what you subscribed to is one weak input among many. Between them sits ranking: your subscriptions, reordered by a model. Where a platform sits determines whether following someone is a decision or a suggestion, and whether publishing reaches an audience you built or enters a lottery. The same axis opens the notes on large general-purpose networks.

2. Whether the graph is symmetric. Symmetric graphs (mutual friendship, mutual connection), require both parties to agree. They stay small, they carry obligation, and they are poor for broadcasting. Asymmetric graphs, following, grow without limit and mean less per link. Many platforms run both simultaneously, with different rules for each, and comparing "friends" on one to "followers" on another is comparing two unrelated things.

3. Whether content is durable or ephemeral. Does something published today still exist, still get found, and still get read in a year? Durable platforms reward work that answers a question someone will ask later; ephemeral ones reward presence and frequency. The shelf life question is the sharpest version of this, because the cost of making one item is highest there. The same effort produces a compounding asset on one and a stream of expiring items on the other.

4. Who owns the audience relationship. If the only route to your audience is the platform's delivery system, the relationship is rented. If you can get a contact route (an address, a feed, a subscription you control), some of it is yours. This is the axis that decides how much a platform's decline actually costs you.

5. What it does with attention. Advertising, subscription, commission, data licensing, recruiting products. Follow the money and you can predict the product's direction. A platform that sells advertising will eventually optimize for time spent; one that sells subscriptions will optimize for the paying user; one that sells access to its members will optimize for the buyer, not the member, which is the pressure described in the notes on what a professional network sells.

6. How moderation works. Central and consistent, central and arbitrary, distributed to volunteers, or effectively absent. Distribution is the whole subject of the notes on federated and protocol-based networks, where there is no single appeal by design. This determines what the place feels like, what happens when something goes wrong, and whether there is an appeal.

Reading a platform against the axes

You can place most services yourself in one session, which is better than trusting a comparison written for someone else.

Building the comparison you actually need

Do not compare platforms in the abstract. Compare them against one job.

Write the job down in a sentence: "reach people who might hire me", "keep a group of forty in touch", "publish work that people find later by searching". Then weight the axes for that job, because they are not equally important for all of them.

Then build a table with your axes as rows and your candidates as columns, and fill it in from your own testing. Six rows you verified beats forty rows you copied.

The numbers problem

Any comparison that leans on user counts is standing on sand. Published figures for the same platform routinely disagree, because they measure different things: registered accounts against active ones, individuals against accounts, self-reported reach against independent survey estimates. Before you use a number to decide anything, find out what it counts.

Where you need audience data, prefer sources that publish their method and definitions. Survey research such as the Pew Research Center's social media fact sheet states its population, sample, and question wording.

That lets you judge whether the figure applies to the people you care about. A number quoted without a definition cannot be checked and should not carry weight.

Traps

Comparing your best platform with someone else's best platform. This is selection bias wearing a case study. Their results come from their audience, their format, and their history. The only comparison that means anything is the same work, from you, on two services.

Treating a recent change as permanent. Distribution behavior shifts. A month of bad numbers is weather; six months in one direction, unaffected by anything you do, is climate.

Confusing an active platform with an active audience. A busy service where nobody engages with your kind of work is worse for you than a quiet one where people do.

Grading on how much you enjoy using it. Real, and worth something, but do not let it masquerade as a finding. Say plainly that you prefer one, rather than constructing reasons.

Assuming the comparison holds for anyone else. These axes are stable; their weights are personal. Publish your weights along with your conclusion or the conclusion is not reusable.

Common questions

How often should a comparison be redone?

Features, constantly, which is why it is not worth comparing them. Axes, rarely: a platform's position on axis 1 or 4 changes maybe once in its life, and when it does it is the biggest thing that will ever happen to the people building on it. Watch for those moves and ignore the rest.

Which axis matters most?

For anyone building an audience, the fourth. Everything else determines how well things go while the platform is working. Audience ownership determines what you keep when it stops.

Can a platform be good on every axis?

No, and the tensions are structural rather than accidental. Durable public archives conflict with member privacy. Recommendation-led discovery conflicts with subscription reliability. A platform that lets you export your audience is undermining the thing it sells. Expect trade-offs and be suspicious of anything claiming none.

What if two platforms score the same?

Then the deciding factor is where the people you need already are, and it is not close. That is also the reason most substitution lists fail their readers. Go where they are and spend the saved effort on the work itself.