Let’s just agree that most of us use dating apps. They have made it easier to meet someone new. But have you wondered what goes behind these algorithms? A viral Reddit thread recently asked employees behind some of the biggest dating apps to spill their secrets: “What’s the most insane user stat or behind-the-scenes fact you found out about?“
And here are some of the answers –
1. I have a friend who works for… I wanna say Tinder. Anyway, the company isn’t important; what is important is that her ENTIRE job is to remove inappropriate images. Her JOB is to look at dick pics all day. Five days a week. That’s all. No stat. Just a weird fucking job.
2. My ex-bf worked for a dating site back in the earlyish 2000s. His job was to pretend to be a woman and message male customers just as their accounts were going to expire. This would encourage them to pay to renew their subscriptions. Once they renewed, he would ghost them.
3. Guys swipe right on 47% of profiles. Women only swipe right on 12%. I knew some guys would swipe right more than women, wasn’t prepared for how little women swipe right!
4. I used to work at Bumble, although this was about 4-5 years ago. Globally, about 90% of the users are men, so there is a huge male to female disparity, although it’s not that bad on a per-country basis (for some countries).
The most depressing stat though was the histogram of word count in messages. Something like 91% of opening messages were just one word “hey”, and ~85% of conversations were just one exchange long (“hey” -> no reply ever).
Looking at the human, digital mating habits splayed out in data science form was really depressing.
5. I ran operations for an online dating company. From database analytics, I can tell you a few things. Men initiate contact around 80% of the time in straight matchmaking. IIRC we were able to determine that it takes on average about 3 dates before sex happens (I don’t recall how we worked that out, I’m not a data analyst, but presumably it was some keyword-based algorithm looking at chat messages
6. We used to create fake accounts and chat with users. It was everything from someone having a premium account that wasn’t getting responses to bored employees.
7. I worked for Match for a couple years. This is probably widely known but women frequently lie about their age and weight and men lie about their height and salary. Also, it’s a big problem that women are inundated with DMs while most men get none.
8. I used to moderate OK Cupid. The amount of unsolicited dick pictures men would send women, not even accompanied by any words was horrifying. I mean, you’d expect it because online dating is a cesspit but the sheer amount would still surprise you.
I had to look at each reported picture and say ” Yes, that’s a penis”.
9. “We had a murder on our platform. The top of the company got interviewed as witnesses. TBH, there wasn’t really anything we did or could have done about it, but it is crazy to think about.”
10. “The algorithms are less sophisticated than you think. … The main goal of the algorithm is always to get you to pay, never to actually ensure you meet somebody in real life, as much as we tried to lie to ourselves that it was.”
11. This is probably going to get buried, I used to work for Shag.co.uk as a content creator. And by that I mean it was me who replied to messages from users, I used to get paid between 11p and 18p per message, with bonuses for hitting certain message amounts.
It all came to an end for me when a guy told me his dad had died that morning, and he wouldn’t be around for a while. I got reviewed for my reply which was “I’m really sorry for your loss if you need to talk I’ll be here for you” they suggested that I should have replied “Do you not care about me? I have needs too” just realized that the site owners really just didn’t give a shit at all.
12. People get scammed often. Lonely people are vulnerable and get scammed out of money and gifts.
13. People are racist, way more than I thought.
14. Requests to rate the app occur after a match is made with the intention of biasing the user to rate the app higher.
15. Our algorithm is intended to maximize ad revenue and time on an app by delivering matches when the probability for the user to churn is highest. The algorithm does not prioritize matches.
What are your thoughts? Let us know in the comment section below!
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