Tinder is full of gorgeous women and well, the profiles just don’t seem right somehow.
Compared to genuine profiles which have ordinary looking women, with profiles that have narrative text, real locations, it’s easy to see which are the fakes and which are real.
Obviously it’s Tinder posting the fake profiles because they want men using their software.
But I wonder is there any way to prove that Tinder is cat fishing it’s own users?
What's far more likely as a source of random bot traffic is that a click farm is using real devices (it can take a bit to get abuse banhammered), and I'm guessing you're in a major metro area where a few bots are more likely to sneak under the radar. Elo[1] is deprecated these days, but much of ranking leans on 2nd- and 3rd-degree evaluation of both yourself and the other person, implying those users are generally evaluated as real and not reported much. Bouncer[2] helps the engine take this kind of data into account.
All this is to say if someone seems suspicious, leverage that Safety Toolkit. But Tinder seeding in fake users themselves would be quite visible externally after a quarter, cause a big spike in reports over time, damage user retention, daily sessions, and stickiness massively, all of which negatively impact ARPPU and paid conversion rates. Throwing tons of standard KPIs isn't worth a fractional boost in sales with the kind of revenue and growth factors[3] they're driving.
[1] https://www.theverge.com/2019/3/15/18267772/tinder-elo-score...
[2] https://techcrunch.com/2015/02/03/tinder-tests-limited-right...
[3] https://ir.mtch.com/overview/default.aspx