Is Tinder crashing and ruining your swipe session? We’ve all been there—just when you’re about to make a match, the app decides to crash. But don’t worry; you’re not alone, and there are several steps you can take to get things back on track. Here’s your comprehensive guide to fixing Tinder crashes with ease.

When I try to open tinder it crashes… any more people with this problem? @Tinder @TinderNL pic.twitter.com/A3KrSrzlXD

— jake (@ryan9900u7) August 15, 2024

Why is Tinder crashing?

Tinder crashes can occur for a variety of reasons. Overloaded or malfunctioning servers can cause the app to become unresponsive. Compatibility issues often arise from outdated device software or app versions, leading to crashes. Additionally, slow or unstable internet connections can disrupt Tinder’s performance, while conflicts with third-party applications can interfere with its operation. Lastly, corrupted or broken installation files may prevent Tinder from functioning properly, resulting in frequent crashes.

Cheater Buster AI reveals if your partner is on Tinder

Fixing the Tinder crashing issues

Experiencing Tinder crashes can be frustrating, but don’t panic just yet! Here are some steps to help you resolve the issue:

• Check your Internet connection: Ensure you have a strong and stable internet connection. Switch between Wi-Fi and mobile data to determine if there’s a connectivity issue.
• Force restart Tinder: Clear the app’s cache and force restart it by going to your device’s settings, selecting “App Manager,” and choosing “Clear Cache” and “Force Stop” for Tinder.
• Update or reinstall Tinder: Make sure you’re using the latest version of Tinder. Update the app through the Google Play Store for Android or the App Store for iOS. If updating doesn’t work, uninstall and then reinstall Tinder to replace any corrupted files.
• Try Tinder.com: If the app continues to crash, access Tinder through your web browser at Tinder.com as an alternative.

• Reboot your phone: Restarting your device can resolve temporary system errors. For Android, hold the power button and select “Restart.” For iOS, use Assistive Touch to find the “Restart” option.
• Check VPN services: If you’re using a VPN, it might be causing conflicts. Try disabling the VPN or use a location spoofing tool if network protection is necessary.
• Contact Tinder support: If the problem persists, reach out to Tinder’s support team. Provide detailed information about the issue so they can assist you effectively.

In the rare event that none of these solutions work, Tinder may be experiencing service degradation. Your patience is appreciated while they work to resolve the issue.

How is data affecting your dating life?

Grindr app keeps crashing too

Similar to Tinder, Grindr also experience crashes due to a range of issues. Server problems, such as overloads or technical difficulties, can cause the app to fail. Outdated software or app versions on your device might result in compatibility issues, leading to crashes. An unstable internet connection can disrupt Grindr’s performance, while interference from third-party apps can cause conflicts. Corrupted or broken installation files can further contribute to the app’s instability.

Why can’t I open Grindr? Or tinder? pic.twitter.com/od8D6zy9Xv

— 𝓙𝓟 (@jmartins05) August 15, 2024

If Grindr is also crashing, these common issues might be affecting its performance.

What algorithms do dating apps use to find your next match? How is your personal data impacting your decision to go on a date? How is AI affecting your dating life?  Find out below.

Technology has changed the way we communicate, the way we move, and the way we consume content. It’s also changing the way we meet people. Looking for a partner online is a more common occurrence than searching for one in person. According to a study by Online Dating Magazine, there are almost 8,000 dating sites out there, so the opportunity and potential to find love is limitless. Besides presenting potential partners and the opportunity for love, these sites have another thing in common — data. Have you ever thought about how dating apps use the data you give them?   

How are dating apps using your data?

All dating applications ask the user for multiple levels of preferences in a partner, personality traits, and preferred hobbies, which raises the question: How do dating sites use this data? On the surface, it seems that they simply use this data to assist users in finding the best possible potential partner. Dating application users are frequently asked for their own location, height, profession, religion, hobbies, and interests. How do dating sites actually use this information as a call to action to find you a match? 

• Natural Language Processing (NLP) looks at social media feeds to make conclusions about users and assess potential compatibility with others. AI programs use this input to look for other users with similar input to present to the user. Furthermore, these programs learn user preferences based on profiles that they agree to or reject. Simply put, the application learns the types of people you are liking and will subsequently put more people like that in front of you to choose from. 
• Deep Learning (DL) sorts through facial features of profiles that you have “liked” or “disliked.” Depending on how homogenous your “likes” are, the variety of options presented to you will change. 

What algorithms are these dating apps using?

Hinge calls itself “the dating app that was designed to be deleted.” It uses a Nobel Prize winning algorithm to put its users together. Known as the Gale-Shipley algorithm, this method looks at users’ preferences, acceptances, and rejections to pair people together. Hinge presents this information to the user with a notification at the top of the screen that lets the person know of high potential compatibility with the given profile. Research shows that since launching this “Most Compatible” feature, Hinge been able to guide its users toward people more suited for them. Research shows that people were eight times more likely to swipe right and agree to a “most compatible” recommendation than the alternative without one. This is ultimately resulting in not only more relationships, but relationships of better quality as well. 

OkCupid’s algorithm uses a similar compatibility feature to match its users together. When filling out a profile for this dating app, users can respond to an extensive questionnaire about their personal traits as well as the traits they are looking for in a partner. For example, someone could report that they are very messy and looking for someone moderately messy. OkCupid would then present the user with potential partners who are moderately messy looking for people who are very messy. The algorithm goes one step further than simple response based matching, it ranks the importance of each trait to pair users as well. This approach must be working because OkCupid was the most mentioned dating app in the New York Times wedding section. 

Not all dating apps use this compatibility approach. Tinder, for instance, relies almost completely  on location and images to suggest potential partners to its users. The other aspect to Tinder’s algorithm is based on a desirability factor. In this case, the more “likes” you get will result in people being presented to you who also get a lot of “likes.” It also works in the opposite circumstance where users who don’t receive a lot of “likes” will be presented with people who also don’t receive a lot of “likes.” As a result, 1.6 billion swipes occur daily on Tinder.

A final example of algorithms in dating apps is how Bumble users can now filter preferences beyond personality traits, professions, and appearances. They are able to filter potential partners  by zodiac signs. In many cultures across the globe, astrological signs have been and continue to be used to measure the compatibility of a couple. Bumble’s AI program takes into account user preferences as well as sign compatibility when presenting a potential partner to its user. Matching zodiac signs is another instance of dating app technology working with user data to create the most compatible matches. The extensiveness of Bumble’s algorithm results in over 60% of matches leading to a conversation. See the chart below for the most popular zodiac signs according to a study of 40 million users by Jaumo. 

Conclusion

AI in dating sites goes beyond the individual’s knowledge of their own personality. It gets to know the users better than they know themselves. By monitoring both user input and user behavior, AI in dating applications truly gets to know the most holistic version of the user. It goes beyond the user’s own notion of themself to reveal truths about the type of partner they are  really looking for. The AI in dating apps aims to reconcile a user’s idealized version of a potential partner with the reality of the types of profiles they like. The trajectory of this revolutionizes the way data will continue to be used in AI mechanisms to help humanity achieve results on multiple platforms, even in dating.

It almost goes without saying that Tinder has taken the dating world by storm. Stats released late last year revealed that Tinder’s 50-million-strong userbase complete over a billion left and right swipes every single day. The success has often been attributed to the fact that Tinder is the closest virtual simulation of the bar experience; you see an attractive person across the bar, and in the that moment- having only seen them, and knowing precious little about them other than the way they look (and maybe their tipple of choice), you decide whether or not to make your approach. It’s virtual speed dating, where every encounter can end in the few moments it takes for you to swipe left or right without your potential partner ever even knowing.

However, another stat released by Tinder exposes that the average user spends 90 minutes a day swiping and reviewing their matches. That is a huge investment in terms of time and effort, without any guarantee you’ll end up matched with anyone.

Objectively the best Tinder profile of all time. 

For Justin Long, a Canadian entrepreneur & Chief of Research for a disruptive technology company, this was the biggest turn-off on Tinder. “Tinder has reached critical mass; I feel it’s been adopted by relatable people and the right variety of women. I became aware of how enjoyable it was to keep matching and swiping for the next match; however, I was dissatisfied with how much time I had to invest in it. Swiping is both Tinder’s best and worst feature.”

His solution? Automate the entire process. Of course, bots have already been created by other Tinder users which swipe right (accept) all possible matches. Whilst inventive, these bots don’t take into account personal preference, or get rid of spammers. Long had something a little more sophisticated in mind- a bot which learns your physical “type” using the Eigenfaces facial recognition algorithm, and automatically got the conversation going with your matches.

The code, dubbed Tinderbox, requires you to make 60 “swipes”- then, the model has enough data to understand your preferences and make auto-pilot matches on your behalf. Long’s blogpost outlines the workflow on the backend like so:

The built-in bot builds facial models using your likes/dislikes
Bot examines profile images, cropping faces
Faces are loaded into an “average” face representing choices
Eigenfaces are computed from average faces
Bot then makes future selections based on Eigenface comparison
Comparisons are essentially k-nearest neighbor selection

The bot first extracts the faces using the Viola-Jones framework, and converts them to greyscale. Photos containing more than one identifiable face are filtered out, to avoid false positives. The pictures are then normalised, and the pixels are converted into a matrix, and used to create single, “average” faces for your “Yes” and “No” swipes for Eigenface comparison. The average face representations look a little something like this:

Implementing the algorithm and trying to find the best matrix library proved to be the trickiest part. “There’s more than one way to bake a cake,” Long says, “and finding the right recipe was difficult.” For those of you interested in the code, here’s a snippet that computes the Eigenfaces matrix using a pixel matrix of multiple images:

[code language=”Python”]/**
* Computes the EigenFaces matrix using a pixel matrix of multiple images.
* @param pixelMatrix
* @param meanColumn
*/
def computeEigenFaces(pixelMatrix: Array[Array[Double]], meanColumn: Array[Double]): DoubleMatrix2D = {
val diffMatrix = MatrixHelpers.computeDifferenceMatrixPixels(pixelMatrix, meanColumn)
val covarianceMatrix = MatrixHelpers.computeCovarianceMatrix(pixelMatrix, diffMatrix)
val eigenVectors = MatrixHelpers.computeEigenVectors(covarianceMatrix)
computeEigenFaces(eigenVectors, diffMatrix)
}

/**
* Computes the EigenFaces matrix for a dataset of Eigenvectors and a diff matrix.
* @param eigenVectors
* @param diffMatrix
*/
def computeEigenFaces(eigenVectors: DoubleMatrix2D, diffMatrix: Array[Array[Double]]): DoubleMatrix2D = {
val pixelCount = diffMatrix.length
val imageCount = eigenVectors.columns()
val rank = eigenVectors.rows()
val eigenFaces = Array.ofDim[Double](pixelCount, rank)

(0 to (rank-1)).foreach { i =>
var sumSquare = 0.0
(0 to (pixelCount-1)).foreach { j =>
(0 to (imageCount-1)).foreach { k =>
eigenFaces(j)(i) += diffMatrix(j)(k) * eigenVectors.get(i,k)
}
sumSquare += eigenFaces(j)(i) * eigenFaces(j)(i)
}
var norm = Math.sqrt(sumSquare)
(0 to (pixelCount-1)).foreach { j =>
eigenFaces(j)(i) /= norm
}
}
val eigenFacesMatrix = new DenseDoubleMatrix2D(pixelCount, rank)
eigenFacesMatrix.assign(eigenFaces)
} [/code]

And computing the distance between two images:

[code language=”Python”] /**
* Computes the distance between two images.
* @param pixels1
* @param pixels2
*/
private def computeImageDistance(pixels1: Array[Double], pixels2: Array[Double]): Double = {
var distance = 0.0
val pixelCount = pixels1.length
(0 to (pixelCount-1)).foreach { i =>
var diff = pixels1(i) – pixels2(i)
distance += diff * diff
}
Math.sqrt(distance / pixelCount)
} [/code]

So Long’s bot is now able to automate all of the swiping. But what about all of those matches that clutter up your notifications, where the person you’ve matched to never replies? Long wanted to go one step further, and identify only the women who genuinely wanted to strike up a conversation. For this, he programmed the bot to start conversations, and use StanfordNLP to analyse the sentiment of responses. “I’ll admit that StanfordNLP’s approach isn’t the best for examining sentiment,” Long confessed. “This is because it tries to analyze the message by its structure and not necessarily by its content. Sarcasm can register as negative (and humor is actually an expression of positive sentiment). Additionally, messages classified as neutral could still be positive – this is because in the bigger picture any message at all still indicates interest. If I were to do this again I would be much more comprehensive.”

If the sentiment was deemed positive, the bot sends out pre-programmed replies. Once three replies have been given, the user receives a conversation that their match is genuinely interested and the conversation is ready to enter. The process looks a little something like this:

Getting the bot up and running didn’t go without a hitch. In terms of technical snags, “The bug where all of my matches were repeatedly messaged tops the cake”, he said. “I had just revised the messaging system and when I went to test it, the bot just harassed all of my chats with constant openers. I think I got a couple spam reports from that. No long-term damage though!”

Still, within 3 weeks, the bot was up and running, and Long was presented with a list of matches who were genuinely interested in speaking with him. But did this actually improve his Tinder experience? Famously, Chris McKinlay spent months using K-modes to hack OkCupid, only to end up on a string of disappointing dates- and then met his future wife when she messaged him out of the blue.

Long’s strategy, I’m happy to report, seems to be more effective. “I made a huge mistake here by not keeping track of my before/after data, so most of my feedback is anecdotal,” he says. “I discovered two huge improvements: 1) massive increase in personal time, and 2) I had a large increase in the quality of my matches. People that I not only found more attractive, but also were better conversationalists.”

He also set up a new account, to see how it would perform with a fresh dataset in 48 hours. In that short time frame, the bot made over 300 “moves” (swipes and messages), established 21 matches, and sparked 4 extended conversations. All this is two days, requiring just 60 swipes from the user.

Of course, the point of TinderBox is not to find love- or whatever else you happen to be looking for. It’s supposed to filter out the hours of wading through incompatible matches, and starting conversations which never get a response. What happens from there is up to you; it’s safe to say that Long made the most it. Speaking about his post-bot dates, he told us “I probably made someone’s night running around the beach in our socks and kissing in the rain. I also came up with one of my best date ideas: bake some cookies, show up at the nearest firehouse, bribe firemen with said cookies, and take a tour.”

His blogpost intimates he’s now turned off the bot. “Admittedly, it worked too well and started to conflict with work. Although in a couple cases I had follow-ups and I’m still seeing one person.”

For those of you curious about TinderBox, Long has uploaded the code to Github for “personal use”. We asked Long how he’d react if he went on a date with a girl who’d used the TinderBox code to set up the match. “I’d be totally shocked because I actually tried using the bot with a female friend previously, and it freaked her out. So she’s either got some balls to be turning over her Tinder account to the bot, or she’s super smart to modify the code and make it work for her. Either way, those are people I want to know.”

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(Image credit: Unsplash)