Context

This is a relaxing mini dataset which explains the match rate of individuals from different universities

Content

1. Segment type : Medium of Usage
2. Segment Description: Name of Universities
3. Answer: Do you use tinder ?
4. Count: Number of Matches
5. Percentage: % of matches

Acknowledgements

DATASET BY ADAM HALPER

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...

Image Credits

source

Context

The Tinder Swindler is a viral British true crime documentary film directed by Felicity Morris and was released on Netflix on 2 February 2022. The film tells the story of the Israeli con artist Simon Leviev who uses the dating-application Tinder to locate individuals he emotionally manipulates into providing financial support for his lavish lifestyle.

Dataset

Hi all, so time for some interesting and fun dataset, the Dataset contains tweets in context to the documentary "The Tinder Swindler"

This dataset contains user reviews and comments from the Bumble dating application on the Google Play Store. Bumble is an online dating app where, in heterosexual matches, female users typically initiate the first contact. Beyond romantic connections, Bumble also facilitates finding friends through "BFF mode" and business networking via "Bumble Bizz". This dataset is valuable for understanding user experiences and sentiment towards the app.

Columns

• reviewId: A unique identifier for each user's review.
• userName: The name of the user who posted the review.
• userImage: A URL to the user's profile image.
• content: The textual comment or feedback provided by the user.
• score: The rating given by the user, ranging from 1 to 5.
• thumbsUpCount: The number of 'thumbs up' or likes a specific comment received.
• reviewCreatedVersion: The version number of the app on which the review was created.
• at: The date and time when the review was created.
• replyContent: The content of any reply made by the Bumble company to the user's comment.
• repliedAt: The date and time when the company's reply was posted.

Distribution

The dataset is typically provided as a data file, often in CSV format. It appears to contain a substantial number of records, with reviewId having 168,651 unique values. The data quality is rated as 5 out of 5, and the version of this dataset is 1.0.

Usage

This dataset is ideal for: * Natural Language Processing (NLP) tasks, such as sentiment analysis of user comments. * Market research to gain insights into user satisfaction and preferences regarding dating apps. * Analysing app performance based on user ratings and feedback. * Studying trends in social networks and popular culture related to online dating. * Identifying common user issues or popular features within the Bumble app.

Coverage

The dataset is global in its geographic scope. The reviews span a time period from 29 November 2015 to 28 June 2025. It primarily covers the experiences of Google Play Store users of the Bumble app. As of June 2016, 46.2% of Bumble's users were female.

License

CC-BY

Who Can Use It

• Data scientists and machine learning engineers interested in text analysis and sentiment modelling.
• App developers seeking direct user feedback to improve application features and user experience.
• Researchers focusing on online dating dynamics, social media behaviour, and popular culture.
• Businesses aiming to understand consumer sentiment and competitive landscapes in the social networking and dating industries.

Dataset Name Suggestions

• Bumble Google Play Reviews
• Bumble App User Feedback
• Bumble Play Store Ratings
• Bumble Dating App Reviews Dataset

Attributes

Original Data Source: Bumble Dating App - Google Play Store Review

Bumble bee occurrence data used for "Climate change winners and losers among North American bumble bees": These data comprise 649 407 specimen records from 48 species and spans 1805–2020. These records have been compiled from a variety of collections and sources with reputable origin. Data contributors are listed in: https://www.leifrichardson.org/bbna.html