Tinder is a dating app that matches users to others based on geographic proximity. It designed (and patented) the swipe interface, in which a user swipes right to ‘like’ or left to...

Tinder's global popularity continues to grow, with the dating app recording around *** million downloads in May 2025. The *********************** region is the largest market for the Tinder app. In May 2025, the app was downloaded more than *** million times. User preferences and trends As Tinder's user base grows, interesting trends emerge in profile content and communication. In 2024, gaming emerged as the fastest-growing interest on Tinder profiles, followed by spa, playlists, and music-related topics. The platform also saw shifts in emoji usage, with the pink or red ribbon emoji gaining popularity. These evolving preferences reflect changing user interests and communication styles within the app. Tinder's competition Tinder.com averaged ******* million monthly visits between April 2022 and January 2024, surpassing other popular dating websites such as Badoo and Bumble. As the online dating market in the United States grows, with over ** million users estimated in 2024, Tinder remains at the forefront of connecting people for both long-term relationships and casual dating.

The Measurable AI Dating App Consumer Transaction Dataset is a leading source of in-app purchases , offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our in-app and email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - User overlap between competitors - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia - EMEA (Spain, United Arab Emirates) - USA - Europe

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Features/subscription plans purchased - No. of orders per user - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact michelle@measurable.ai for a data dictionary and to find out our volume in each country.

According to a survey conducted in March 2025, ** percent of Tinder users in the United States identified as men, and ** percent identified as women. As of April 2025, Tinder is the most used dating app worldwide.

In 2013, Tinder revolutionised the online dating industry with a simple system, swipe right if interested, left if not. Instead of having a matchmaker rifle through thousands of profiles to find...

After a strenuous year fighting a lawsuit against the company she co-founded, Tinder co-founder Whitney Wolfe Herd joined Russian entrepreneur and Badoo founder Andrey Andreev to launch Bumble....

Discover the booming dating app market! This in-depth analysis reveals key trends, growth drivers, and leading players shaping the future of online dating, from Tinder and Bumble to niche platforms. Explore market size, CAGR, and regional insights from 2019-2033.

According to a survey conducted in March 2025, ** percent of Tinder users in the United States belonged to Generation Z, and ** percent were millennials. Overall, ** percent were of Gen X, and around *** percent of all Tinder users in the U.S. were Baby boomers.

Link to github repo: https://github.com/Ashley-Xu/AIPIFinalDataset

The dataset is intended for research, analysis, and innovation. All efforts have been made to anonymize the data to ensure that no individual can be identified or harmed by its use.

Every effort has been made to uphold the privacy and confidentiality of the individuals whose data is represented in the dataset.

The dataset may only be used for ethical, responsible, and non-exploitative purposes. Users of the dataset are expected to:

Avoid any attempt to re-identify individuals or misuse the data in ways that could lead to harm, discrimination, or stigmatization. Adhere to applicable laws, regulations, and ethical guidelines relevant to data usage and research in their jurisdiction. Cite the dataset responsibly in any derivative work to maintain transparency about its source and limitations.

The dataset may reflect biases inherent in the original data source and population. Researchers are encouraged to be aware of these limitations and to take steps to address or acknowledge biases in their work to prevent misleading conclusions or harmful applications.

While every effort has been made to anonymize the data and protect privacy, no anonymization process is entirely risk-free. Users of the dataset assume full responsibility for ensuring that their research complies with ethical and legal standards.

The data was collected from https://www.swipestats.io/ with appropriate permissions from the owner of the website.

Open source license Community Data License Agreement – Sharing – Version 1.0

Context

This is a relaxing mini dataset which explains the match rate of individuals from different universities and whether the app has helped the person to find relationship

Content

1. ID -User id
2. Segment type : Medium of Usage
3. Segment Description: Name of Universities
4. Answer: Do you use tinder ?
5. Count: Number of Matches
6. Percentage: % of matches
7. It became a relationship- Success of relationship (Target)

Acknowledgements

DATASET BY ADAM HALPER

The in-app purchase revenue of the dating platform Tinder has skyrocketed in recent years. The total worldwide revenue amounted to nearly 82.64 million U.S. dollars in June 2024. Of these, **** millions were generated by users based in North and Latin America, while users in Europe, the Middle East and Africa generated **** million U.S. dollars in revenues in the most recently measured month of 2024.

Foreword

This dataset is a preview of a bigger dataset. My Telegram bot will answer your queries for more data and also allow you to contact me.

Context

When Dating apps like Tinder were becoming viral, people wanted to have the best profile in order to get more matches and more potential encounters. Unlike other previous dating platforms, those new ones emphasized on the mutuality of attraction before allowing any two people to get in touch and chat. This made it all the more important to create the best profile in order to get the best first impression.

Parallel to that, we Humans have always been in awe before charismatic and inspiring people. The more charismatic people tend to be followed and listened to by more people. Through their metrics such as the number of friends/followers, social networks give some ways of "measuring" the potential charisma of some people.

In regard to all that, one can then think: - what makes a great user profile ? - how to make the best first impression in order to get more matches (and ultimately find love, or new friendships) ? - what makes a person charismatic ? - how do charismatic people present themselves ?

In order to try and understand those different social questions, I decided to create a dataset of user profile informations using the social network Lovoo when it came out. By using different methodologies, I was able to gather user profile data, as well as some usually unavailable metrics (such as the number of profile visits).

Content

The dataset contains user profile infos of users of the website Lovoo.

The dataset was gathered during spring 2015 (april, may). At that time, Lovoo was expanding in european countries (among others), while Tinder was trending both in America and in Europe. At that time the iOS version of the Lovoo app was in version 3.

Accessory image data

The dataset references pictures (field pictureId) of user profiles. These pictures are also available for a fraction of users but have not been uploaded and should be asked separately.

The idea when gathering the profile pictures was to determine whether some correlations could be identified between a profile picture and the reputation or success of a given profile. Since first impression matters, a sound hypothesis to make is that the profile picture might have a great influence on the number of profile visits, matches and so on. Do not forget that only a fraction of a user's profile is seen when browsing through a list of users.

https://s1.dmcdn.net/v/BnWkG1M7WuJDq2PKP/x480" alt="App preview of browsing profiles">

Details about collection methodology

In order to gather the data, I developed a set of tools that would save the data while browsing through profiles and doing searches. Because of this approach (and the constraints that forced me to develop this approach) I could only gather user profiles that were recommended by Lovoo's algorithm for 2 profiles I created for this purpose occasion (male, open to friends & chats & dates). That is why there are only female users in the dataset. Another work could be done to fetch similar data for both genders or other age ranges.

Regarding the number of user profiles It turned out that the recommendation algorithm always seemed to output the same set of user profiles. This meant Lovoo's algorithm was probably heavily relying on settings like location (to recommend more people nearby than people in different places or countries) and maybe cookies. This diminished the number of different user profiles that would be presented and included in the dataset.

Inspiration

As mentioned in the introduction, there are a lot of questions we can answer using a dataset such as this one. Some questions are related to - popularity, charisma - census and demographic studies. - Statistics about the interest of people joining dating apps (making friends, finding someone to date, finding true love, ...). - Detecting influencers / potential influencers and studying them

Previously mentioned: - what makes a great user profile ? - how to make the best first impression in order to get more matches (and ultimately find love, or new friendships) ? - what makes a person charismatic ? - how do charismatic people present themselves ?

Other works: - A starter analysis is available on my data.world account, made using a SQL query. Another file has been created through that mean on the dataset page. - The kaggle version of the dataset might contain a starter kernel.

Discover the booming online dating market! Explore key trends, growth drivers, and leading companies shaping this multi-billion dollar industry. Learn about market segmentation, regional variations, and future projections in our comprehensive analysis.

Discover the booming online dating app market! This comprehensive analysis reveals market size, growth trends (CAGR 15%), key players (Tinder, Bumble, Hinge), regional insights, and future projections to 2033. Learn about the challenges and opportunities in this rapidly evolving industry.

Hinge is one of a new wave of apps that have marketed themselves as the anti-Tinder, with a focus on relationships over casual hookup culture. Its tagline, “the dating app designed to be...

The dataset included here belongs to the project ‘Finding Love in the Black Box: Algorithm Awareness on Dating Apps’ by Bukman, Sharabi and Timmermans (2024). The aim was to assess the level of algorithm awareness among Dutch and Belgian users of the dating apps Breeze (Dutch) and Tinder, and test whether it is associated with high algorithm trust and satisfaction with matches.BACKGROUND Most dating apps incorporate artificial intelligence (AI) to match users to one another but are not fully transparent about how their recommender systems work. In turn, users have varying degrees of awareness of matching algorithms. Unfortunately, little is known about how insight into algorithmic functioning affects the online dating experience. This research examined the relationship between algorithm awareness, trust in the recommender system, and satisfaction with the quality of matches.METHOD A survey was conducted among 216 users of U.S.-based Tinder and 500 users of the Dutch dating app Breeze. Relationships were estimated with structural equation modeling, and effect sizes were compared between these two groups of users to assess variability based on dating app affordances. Higher algorithm awareness was associated with more trust in the recommender system, which in turn positively related to the ease by which users could find matches. The dataset is a composite of two separate collections. The data among Breeze users was collected in collaboration with Breeze itself. Users received a popup in the app with the link to the survey. 688 people participated, which resulted in 500 complete responses in two days. The data for Tinder users was collected in a period of several weeks on various unrelated social media pages. After 500 respondents started the survey, 216 complete responses remained. All participants had to be over 18 years old, and had to have used the app in the previous 30 days.RESULT The presence of this fully mediated effect between awareness and satisfaction with matches suggested that knowledge of matching algorithms can affect both the perception of the recommender system as well as the results of using dating apps. In other words, users with higher algorithm awareness are more content with the quality of the profiles they match with, mediated by a higher trust in the abilities of matching algorithms. No significant differences were found between groups.CONCLUSION Overall, the results indicate that increasing the awareness that users have of algorithm functioning could improve the online dating experience. This research contributes to the limited literature on human-AI interaction in the context of dating apps.

Lovoo v3 Dating App User Profiles and Statistics

Revealing popular user traits and behavior

By Jeffrey Mvutu Mabilama [source]

About this dataset

When Dating apps like Tinder began to become more popular, users wanted to create the best profiles possible in order to maximize their chances of being noticed and gain more potential encounters. Unlike traditional dating platforms, these new ones required mutual attraction before allowing two people to chat, making it all the more important for users to create a great profile that would give them an advantage over others.

It was amidst this scene that we Humans began paying attention at how charismatic and inspiring people presented themselves online. The most charismatic individuals tended to be the ones with the most followers or friends on social networks. This made us question what makes a great user profile and how one could make a lasting first impression in order ensure finding true love or even just some new friendships? How do we recognize a truly charismatic person from their presentation on social media? Is there any way of quantifying charisma?

In 2015 I set out with researching all this using Lovoo's newest dating app version -V3 (the iOS version), gathering user profile data such as age demographics, interest types (friendship, chatting or dating), language preferences etc., as well as usually unavailable metrics like number of profile visits, kisses received etc. I was also able to collect pictures of those user profiles in order discern any correlations between appeal and reputation that may have existed at that time amongst Lovoo's population base.

My goal is forthis dataset will help you answer those questions related not just romantic success but also popularity/charisma censes/demographic studies and even detect influential figures both within & outside Lovoo's platform . A starter analysis is available accompanying this dataset which can be used as a reference point when working with the data here. Using this dataset you can your own investigations into:

  * What type of person has attracted more visitors or potential matches than others?   
  * Which criteria can be used when determining someone’s charm/likability among others    ?
  * How does one optimize his/her dating app profile visibility so he/she won’t remain unseen among other users? 

Grab this amazing opportunity now! Kick-start your journey towards understanding the inner workings behind success in online relationships today!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To get started with this dataset first you need to download it from Kaggle. Once downloaded you should take a look at the column names in order to get an idea of what information is available. This data includes fields such as gender, age name (and nickname), number of pictures uploaded/profile visits/kisses /fans/gifts received and flirt interests (chatting or making friends). It also contains language specifics like detected languages for each user as well as country & city of residence.

The most interesting section for your research is likely the number of details that have been filled in for each user – such as whether they are interested in chatting or making friends. Usually these information points allow us to infer more about a person’s character – from jokester to serious individualist (or anything else!). The same holds true for their language preferences which might reveal aspects regarding their cultures orientation or habits.

You may also want collected data which was left out here - imagery associated with users' profiles - so please contact JfreexDatasets_bot on Telegram if you would like access to this imagery that has not yet been uploaded here on Kaggle but is intregral part of understanding what makes a great user profile attractive on these platforms according Aesthetics Theory applied in an uthentic way when considering how each image adds sentimental appeal value by its perspective content focus - be it visually descriptive; emotive narrative; personality coupled with expression mood association.. etcetera... Or simple just download relevant images yourself using automated scripts ready made via webiste Grammak where Github Repo exists: https://github.com/grammak580542008/Lovoo-v3-Profiles-Data # 1 year ago...

Finally moving ahead — keep in mind that there are other ways data can be gathered possible besides just downloading it from Kaggle – such us Messenger Bots or Customer Relationship Management systems which help companies serve...

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13364933%2F23694fae55e2e76299358693ba6f32b9%2Flv-share.jpg?generation=1684843825246772&alt=media" alt=""> ➡️ There are total 3 datasets containing valuable information. ➡️ Understand people's fame and behavior's on a dating app platform. | Column Name | Description | |---------------------|------------------------------| | Age | The age of the user. | | Number of Users | The total number of users. | | Percent Want Chats | Percentage of users who want chats. | | Percent Want Friends| Percentage of users who want friendships. | | Percent Want Dates | Percentage of users who want romantic dates. | | Mean Kisses Received| Average number of kisses received by users. | | Mean Visits Received| Average number of profile visits received by users. | | Mean Followers | Average number of followers for each user. | | Mean Languages Known| Average number of languages known by users. | | Total Want Chats | Total count of users interested in chats. | | Total Want Friends | Total count of users looking for friendships. | | Total Want Dates | Total count of users seeking romantic dates. | | Total Kisses Received| Overall count of kisses received by users. | | Total Visits Received| Overall count of profile visits received by users. | | Total Followers | Overall count of followers for all users. | | Total Languages Spoken| Total count of languages spoken by all users. |

SUMMARY

When Dating apps like Tinder were becoming viral, people wanted to have the best profile in order to get more matches and more potential encounters. Unlike other previous dating platforms, those new ones emphasized on the mutuality of attraction before allowing any two people to get in touch and chat. This made it all the more important to create the best profile in order to get the best first impression.

Parallel to that, we Humans have always been in awe before charismatic and inspiring people. The more charismatic people tend to be followed and listened to by more people. Through their metrics such as the number of friends/followers, social networks give some ways of "measuring" the potential charisma of some people.

In regard to all that, one can then think:

what makes a great user profile ? how to make the best first impression in order to get more matches (and ultimately find love, or new friendships) ? what makes a person charismatic ? how do charismatic people present themselves ? In order to try and understand those different social questions, I decided to create a dataset of user profile informations using the social network Lovoo when it came out. By using different methodologies, I was able to gather user profile data, as well as some usually unavailable metrics (such as the number of profile visits).

Content

The dataset contains user profile infos of users of the website Lovoo.

The dataset was gathered during spring 2015 (april, may). At that time, Lovoo was expanding in european countries (among others), while Tinder was trending both in America and in Europe. At that time the iOS version of the Lovoo app was in version 3.

Accessory image data The dataset references pictures (field pictureId) of user profiles. These pictures are also available for a fraction of users but have not been uploaded and should be asked separately.

The idea when gathering the profile pictures was to determine whether some correlations could be identified between a profile picture and the reputation or success of a given profile. Since first impression matters, a sound hypothesis to make is that the profile picture might have a great influence on the number of profile visits, matches and so on. Do not forget that only a fraction of a user's profile is seen when browsing through a list of users.

https://s1.dmcdn.net/v/BnWkG1M7WuJDq2PKP/x480

Details about collection methodology In order to gather the data, I developed a set of tools that would save the data while browsing through profiles and doing searches. Because of this approach (and the constraints that forced me to develop this approach) I could only gather user profiles that were recommended by Lovoo's algorithm for 2 profiles I created for this purpose occasion (male, open to friends & chats & dates). That is why there are only female users in the dataset. Another work could be done to fetch similar data for both genders or other age ranges.

Regarding the number of user profiles It turned out that the recommendation algorithm always seemed to output the same set of user profiles. This meant Lovoo's algorithm was probably heavily relying on settings like location (to recommend more people nearby than people in different places or countries) and maybe cookies. This diminished the number of different user profiles that would be pr...

Discover the booming online dating market! Explore industry trends, projected growth (CAGR), key players (Match Group, Bumble, etc.), and regional market share analysis in this comprehensive report covering the period 2019-2033. Learn about the driving forces and challenges shaping the future of online romance.

Traffic analytics, rankings, and competitive metrics for tinder.com as of January 2026