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!

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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...

Context Bumble is an online dating application. Profiles of potential matches are displayed to users, who can "swipe left" to reject a candidate or "swipe right" to indicate interest. In heterosexual matches, only female users can make the first contact with matched male users, while in same-sex matches either person can send a message first. The app is a product of Bumble Inc.

Users can sign up using their phone number or Facebook profile, and have options of searching for romantic matches or, in "BFF mode", friends. Bumble Bizz facilitates business communications. Bumble was founded by Whitney Wolfe Herd shortly after she left Tinder, a dating app she says she co-founded, due to growing tensions with other company executives. Wolfe Herd has described Bumble as a "feminist dating app". As of January 2021, with a monthly user base of 42 million, Bumble is the second-most popular dating app in the U.S. after Tinder. According to a June 2016 survey, 46.2% of its users are female. According to Forbes, by 2017 the company was valued at more than $1 billion, and the company reports having over 55 million users in 150 countries as of 2019.[Source: Wikipedia]

This dataset belongs to the Bumble app available on the Google Play Store. The Dataset mostly has user reviews and the various comments made by the users.

Content The content of the various columns is listed below. Please find the description for each column.

Column Name Column Description userName Name of a User userImage Profile Image that a user has content This represents the comments made by a user score Scores/Rating between 1 to 5 thumbsUpCount Number of Thumbs up received by a person reviewCreatedVersion Version number on which the review is created at Created At replyContent Reply to the comment by the Company repliedAt Date and time of the above reply reviewId unique identifier Acknowledgements Banner image - Bumble

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

Problem Statement Tinder is a casual dating site that allows users to make split-second decisions to determine if they like a potential match. The user swipes right on the profile to match the potential suitor. If the potential suitor also swipes right, a match is made and both parties are alerted.

Tinder is a massive phenomenon in the online dating world. Because of its vast user base, it potentially offers lots of data that is exciting to analyze.

We have collected a small dataset which explains the match rate of the individuals from different universities, and whether the app has helped them find a relationship.

About the Data The dataset contains information about the match rate of the individuals from different universities, and whether the app (i.e. Tinder) has helped them find a relationship.

Data Description ID : User id Segment type : Medium of Usage Segment Description : Name of Universities Answer : Do you use tinder ? Count : Number of Matches Percentage : % of matches (the value ranges from 0 to 1 as it is not multiplied with 100) It became a relationship : Success of relationship (Target)

BackgroundUniversity campus clinics provide crucial sexual health services to students, including STI/HIV screening, testing, contraception, and counseling. These clinics are essential for engaging young adults who may lack access to primary care or have difficulty reaching off-campus services. Dating apps are widely used by young adults, yet there is a lack of studies on how they affect sexual practices. This study aimed to evaluate the use of dating apps, engagement in condomless sexual activity, and the prevalence of STIs among young adult college students in Northern Texas.MethodsA cross-sectional survey was conducted from August to December 2022 among undergraduate and graduate students aged 18–35 at a large university in Northern Texas. A total of 122 eligible participants completed the survey, which assessed demographics, sexual behaviors, dating app use, and STI/HIV testing practices. Descriptive statistics, bivariate analyses, and multivariate Poisson regression analyses with robust variance were performed to identify factors associated with dating app use and condomless sexual activity.ResultsTwo-thirds of participants reported using dating apps. Significant differences were found between app users and non-users regarding demographic factors and unprotected sexual behaviors. Dating app users were more likely to report multiple sexual partners, inconsistent condom use, and a higher likelihood of engaging in unprotected sex. Poisson regression analysis indicated that app use was associated with residing in large urban areas, frequent use of campus STI/HIV screening services, and having multiple sexual partners (p 

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our 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 - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

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

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - 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 business@measurable.ai for a data dictionary and to find out our volume in each country.

The Measurable AI Temu & Fast Fashion E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our 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 - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan, Thailand, Malaysia, Vietnam, Indonesia, Singapore, Hong Kong, Phillippines) - EMEA (Spain, United Arab Emirates, Saudi, Qatar) - Latin America (Brazil, Mexico, Columbia, Argentina)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more - Email ID (can work out user overlap with peers and loyalty)

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018.

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 business@measurable.ai for a data dictionary and to find out our volume in each country.

ABSTRACT Objectives: analyze the association between the level of HIV knowledge among young people from Amazonas region, their sociodemographic profile and infection risk factors. Methods: cross-sectional analytical study, which used a structured questionnaire containing questions about sociodemographic, behavioral aspects and HIV knowledge. Data were grouped by sex and underwent ordinal and binary logistic regression analysis. Results: the students had an HIV knowledge deficit, associated with a low educational level of their parents and low family income. The most frequent risk factors were lack of knowledge on the part of female students regarding proper male condom use, their infrequent use in sexual relations and failure to do HIV testing. There was an association between level of knowledge and use of dating apps by female students. Conclusions: there was no association between level of knowledge and the preponderant risk factors, but the students’ knowledge deficit rendered them more vulnerable to infection.