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How to find tinder history

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Since the new GDPR rules for businesses have taken effect, you have the right to request to see what data a company has been collecting on you that you supplied. But what about Tinder? Is that all? I have requested and downloaded my own data from a recent test account, and this is what they had on me , or could have had on me if I had provided the information empty fields in the report :. Most of this was to be expected, but noteworthy are the saved IP address and phone number. Perhaps more interesting is what data could have been expected, but is missing from this list.


I asked Tinder for my data. It sent me 800 pages of my deepest, darkest secrets

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My mind went completely blank, an effect that being given such free reign over choosing almost anything generally has on me. A few days later, I received the below message on one of my group WhatsApp chats:. This sparked an idea. Thus, my project idea was formed.

The next step? Tell my girlfriend…. A few Tinder facts, published by Tinder themselves:. Problem 1: Getting data. But how would I get data to analyse? After a bit of googling, I came across this article:. This lead me to the realisation that Tinder have now been forced to build a service where you can request your own data from them, as part of the freedom of information act.

Once clicked, you have to wait 2—3 working days before Tinder send you a link from which to download the data file. I eagerly awaited this email, having been an avid Tinder user for about a year and a half prior to my current relationship.

After what felt like an age, the email came. The data was thankfully in JSON format, so a quick download and upload into python and bosh, access to my entire online dating history.

The Data. The data file is split into 7 different sections:. Problem 2: Getting more data. But how do I do this…. Cue a non-insignificant amount of begging. Miraculously, I managed to persuade 8 of my friends to give me their data. The biggest success? My girlfriend also gave me her data. I settled on the definition being either a number was obtained from the other party, or a the two users went on a date.

I then, through a combination of asking and analysing, categorised each conversation as either a success or not. Problem 3: Now what? Cleaning is dull, but is also critical to be able to extract meaningful results from the data.

Problem 4: Different email addresses lead to different datasets. When you sign up for Tinder, the vast majority of people use their Facebook account to login, but more cautious people just use their email address. Alas, I had one of these people in my dataset, meaning I had two sets of files for them. This was a bit of a pain, but overall not too difficult to deal with. Having imported the data into dictionaries, I then iterated through the JSON files and extracted each relevant data point into a pandas dataframe, looking something like this:.

Now that the data was in a nice format, I managed to produce a few high level summary statistics. The dataset contained:. And thus, with the data in a nice format, the exploration could begin! The Exploration. I did this by plotting a few charts, ranging from simple aggregated metric plots, such as the below:.

The first chart is fairly self explanatory, but the second may need some explaining. The idea of this plot was to try to understand how people use the app in terms of messaging more than one person at once. I initially started looking at various metrics over time split out by user, to try to determine any high level trends:. I then decided to look deeper into the message data, which, as mentioned before, came with a handy time stamp. Having aggregated the count of messages up by day of week and hour of day, I realised that I had stumbled upon my first recommendation.

I then started looking at length of message in terms of both words and letters, as well as number of messages per conversation. But once you start to digging, there are a few clear trends:. These observations lead to my second and third recommendations. One caveat here is that the data contains links , which count as long words, so this may skew the results. Anywhere between your 20th and 30th message is best. But I knew absolutely nothing about how to do that. I spent some time researching the topic, and discovered that the nltk sentiment.

This works by giving the user four scores, based on the percentage of the input text that was:. Luckily, it also deals with things such as word context, slang and even emojis. As I was looking at sentiment, no pre-processing was done lower-casing, removal of punctuation etc. I then split the conversations down into their constituent messages and fed them through one at a time, averaging the scores up to conversation level.

This produced a much more realistic outcome in my opinion:. Be positive, but not too positive. The average sentiment for a successful conversation was 0.

Having said that, being too positive is almost as bad as being too negative. The final alley I explored was what effect various details about the first message had on the success of the conversation. Initial thoughts of things that could have an effect were:. As an extension, you double your probability of success by not just using a one word opener e.

No Successes. First message sentiment turned out to be about 0. Now onto explicit content … Another one that had the potential of being quite tricky, as there are no built in libraries that pick up use of expletives etc. Luckily I stumbled upon this:. I can assure you, there are some absolute crackers contained within it. I then checked to see which first messages contained a word from this list, 40 of which did. As is always the case with things like this, i found some interesting edge-cases:.

FYI this was a bloke talking about his rowing leggings…. This lead me to my fifth and final recommendation. When sending a first message:.

Thanks for reading, any ideas for future work would be much appreciated! Sign in. My friends gave me their Tinder data…. Jack Ballinger Follow. I asked Tinder for my data. It sent me pages of my deepest, darkest secrets The dating app knows me better than I do, but these reams of intimate information are just the tip of the iceberg.

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Tinder Information, Statistics, Facts and History

Tinder is always looking for ways to add value to the app while parting you from your money and Likes You is one of the more recent features. Available as part of the Tinder Gold subscription, Likes You is a neat feature that shows you who has liked you before you have to like them back. Released sometime in , this feature is like a shortcut to dating. If you subscribe to Tinder Gold, Likes You gives you a special page where you see a grid of the people that have already swiped right on you.

Tinder users experienced an app crash and issues with their messages and matches Tuesday morning that was continuing into the afternoon hours. First users were having a hard time logging into the app, many reported getting an error message and denied from logging into the app. Others then said even once they were in the app, their matches and their messages appeared to be missing.

Tinder is a dating app that matches users to others based on geographic proximity. They can also see age, and if they have any Facebook connections in common. The Tinder app is built around the idea of the double opt-in — taking out the element of embarrassment and unwanted attention. You can only talk to someone if you both like each other. IAC is also responsible for dating sites Match.

Tinder Matches and Messages Disappear, Not Showing Up, Loading in App After Crash

Keep reading! The inner workings of this popular dating app's algorithm is a closely guarded secret, and the source of much speculation. Elo Score Tip 1: Be mindful with your swipes. Tinder wants you to be selective, so do that moving forward. Your rating matters, because this number helps determine which profiles you see — and who sees your profile. It also determines where your profile ends up in their card stack. This should be obvious, since your pictures are the single biggest factor in which way someone will swipe on your profile.

How Hinge plays with your psychology to get you a match

The dating app knows me better than I do, but these reams of intimate information are just the tip of the iceberg. What if my data is hacked — or sold? I recall a few of them very well: the ones who either became lovers, friends or terrible first dates. But Tinder has not. The dating app has pages of information on me, and probably on you too if you are also one of its 50 million users.

In that moment, how do you feel? Do you confidently walk up to them, or do you stand there frozen never really making a move.

My mind went completely blank, an effect that being given such free reign over choosing almost anything generally has on me. A few days later, I received the below message on one of my group WhatsApp chats:. This sparked an idea.

My friends gave me their Tinder data…

Tinder is part of IAC -- one of the most diversified groups of companies on the internet -- and is one of the most popular dating apps , right next to OkCupid and Bumble. Tinder recently introduced a new algorithm that alternates the photo first seen by others when you show up on Tinder. The app then notes each response as others swipe on you and reorders the photos to show your most popular ones first.

Recovering matches and conversations after deleting Tinder self. Matches and conversations are not recoverable. You will see them gain when you swipe though, but they have to swipe right again to initiate conversation again. Use of this site constitutes acceptance of our User Agreement and Privacy Policy. All rights reserved. Tinder comments.

Tinder Revenue and Usage Statistics (2020)


I first discovered Tinder when I was studying at the university. All my guy friends where huddled as we wait for time to pass by to go to our next class. As the nosy.




How do I request a copy of my personal data?






Comments: 3
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