250 people moderating AND supporting 190m daily active users??

Let's do some napkin math here:

500 million tweets, let's say 10% are reported, and evenly distributed. That would make for reviewing 200,000 tweets per employee per day. That's 7 tweets that need to be moderated per second for a standard 8 hour day.

190m active users, .1% of which need support daily. On top of 7 tweets per second, that's just shy of three user support tickets per second that they need to manage as well.

And that's without weekends, holidays, sick days, etc.

EDIT: Let's go the other way with napkin math too.

Let's say each content moderator can review 6 tweets per minute.

50m tweets to be moderated at that velocity means you need around 136,000 man hours per day. For an 8 hour day, that's 17,000 employees. Too many.

So you make an ML algorithm that processes the reported tweets, but needs backup and spot checking on 10% of those. 1,700 content moderators (and a team dedicated to building/maintaining the ML report checking algorithm).

Now, about those support requests. Based on my experience from being a CSR for Amazon years ago, you'll probably want 2-3 minutes per support request, and that's if you're sending a form letter back. 1.9m requests at 20 per hour means some 95,000 man-hours per day, or 12k CSRs. Too many. Another ML algorithm (and team to maintain it) and we're down to 1,200 CSRs.

About 3 thousand employees plus their support structure, just to handle tweet reports and CSR requests.

Based on napkin math, 7,000 employees makes a lot of sense to me.

The idea that 10% of tweets are reported is a huge over estimate. I'd say at most 1% of tweets are reported, and it's probably more like 0.1%.

Twitters actual numbers (from https://transparency.twitter.com/en/reports/rules-enforcemen...) show that 11.6m reports were generated in the period July to December 2001, which is roughly 65,000 reports per day. ML could easily reduce this number further, but with 100 employees doing moderation, even without ML, that's 650 reports per day. That's getting towards doable.

Are those employees able to speak all the world's languages?

> ML could easily reduce this number further

Seeing how poorly ML works for moderation (Too many false positives), I don't think it belongs anywhere near it.

The problem is that you could offer a user a path to request a human review moderation action taken by ML, but bad actors that knowingly break rules will just request human review and at that point, the ML is worthless.

10% would also include automatically reviewed tweets for misinformation, covid, and any other language filters they have in place.

Not all reviewed-for-moderation tweets would happen because of user reports.

Isn't the grunt work of moderation mostly outsourced today anyway, meaning those employees aren't on Twitter's books?

Exploring this further. I don't have real numbers but I suspect yours are pretty far off.

10% reported seems high, possibly by as much as an order of magnitude. The overwhelming majority of tweets are vapid and innocuous.

It seems like an ML algorithm could do better than 90%. It doesn't have to be as perfect as driving a car; if it screws up occasionally it will merely annoy users (and probably the users that are most troublesome).

If twitter becomes a more permissive environment, less censorship is necessary. Agree or disagree with it, it means less work for the censors.

Paid subscriptions give you a significant new trust metric for users.

The rest you can farm out to mechanical turk?

250 seems within the realm of possibility. People will complain about you the same way they complain about Google, but they'll keep using your product.

I pointed this out in a sibling, but 10% would also include automatically reviewed tweets for misinformation, covid, and any other language filters they have in place.

Also, having worked for a company that did ML sentiment analysis and content analysis against the twitter firehose, the accuracy of ML was closer to 65% than it was 99%. Yes, the company had a huge in-house crew dedicated to checking that 35%, and monitoring twitter manually for anything missed.

You guys have never worked at a major social media platform and it shows. It takes around that many engineering staff just to run a passable advertising platform. There are so many technical nuances that you cannot even imagine.

For context, when I worked at _The Guardian_, roughly half our engineering staff worked in Commercial - doing things for advertisers