About this Dataset

Dating App Fame & Behavior

➡️ 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 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:

Tables

Interests Of Users By Age

@kaggle.utkarshx27_lovoo_dating_app_dataset.interests_of_users_by_age

12.9 KB
11 rows
16 columns


CREATE TABLE interests_of_users_by_age (
  "age" BIGINT,
  "nbusers" BIGINT,
  "percentwantchats" DOUBLE,
  "percentwantfriends" DOUBLE,
  "percentwantdates" DOUBLE,
  "meankissesreceived" BIGINT,
  "meanvisitsreceived" DOUBLE,
  "meanfollowers" DOUBLE,
  "meanlanguagesknown" DOUBLE,
  "totalwantchants" BIGINT,
  "totalwantfriends" BIGINT,
  "totalwantdates" BIGINT,
  "totalkissesreceive" BIGINT,
  "totalvisitsreceived" BIGINT,
  "totalfollowers" BIGINT,
  "totallanguagesspoken" BIGINT
);

Lovoo V3 Users Api Results

@kaggle.utkarshx27_lovoo_dating_app_dataset.lovoo_v3_users_api_results

445.84 KB
3973 rows
42 columns


CREATE TABLE lovoo_v3_users_api_results (
  "gender" VARCHAR,
  "genderlooking" VARCHAR,
  "age" BIGINT,
  "name" VARCHAR,
  "counts_details" DOUBLE,
  "counts_pictures" BIGINT,
  "counts_profilevisits" BIGINT,
  "counts_kisses" BIGINT,
  "counts_fans" BIGINT,
  "counts_g" BIGINT,
  "flirtinterests_chat" BOOLEAN,
  "flirtinterests_friends" BOOLEAN,
  "flirtinterests_date" BOOLEAN,
  "country" VARCHAR,
  "city" VARCHAR,
  "location" VARCHAR,
  "distance" DOUBLE,
  "isflirtstar" BIGINT,
  "ishighlighted" BIGINT,
  "isinfluencer" BIGINT,
  "ismobile" BIGINT,
  "isnew" BIGINT,
  "isonline" BIGINT,
  "isvip" BIGINT,
  "lang_count" BIGINT,
  "lang_fr" BOOLEAN,
  "lang_en" BOOLEAN,
  "lang_de" BOOLEAN,
  "lang_it" BOOLEAN,
  "lang_es" BOOLEAN,
  "lang_pt" BOOLEAN,
  "verified" BIGINT,
  "shareprofileenabled" BIGINT,
  "lastonlinedate" VARCHAR,
  "lastonlinetime" DOUBLE,
  "birthd" BIGINT,
  "crypt" DOUBLE,
  "freetext" VARCHAR,
  "whazzup" VARCHAR,
  "pictureid" VARCHAR,
  "userid" VARCHAR,
  "issystemprofile" DOUBLE
);

Lovoo V3 Users Instances

@kaggle.utkarshx27_lovoo_dating_app_dataset.lovoo_v3_users_instances

329.06 KB
2919 rows
39 columns


CREATE TABLE lovoo_v3_users_instances (
  "gender" VARCHAR,
  "age" BIGINT,
  "name" VARCHAR,
  "counts_pictures" BIGINT,
  "counts_profilevisits" BIGINT,
  "counts_kisses" BIGINT,
  "flirtinterests_chat" BOOLEAN,
  "flirtinterests_friends" BOOLEAN,
  "flirtinterests_date" BOOLEAN,
  "connectedtofacebook" BOOLEAN,
  "isvip" BOOLEAN,
  "isverified" BOOLEAN,
  "lastonline" TIMESTAMP,
  "lastonlinets" DOUBLE,
  "lang_count" BIGINT,
  "lang_fr" BOOLEAN,
  "lang_en" BOOLEAN,
  "lang_de" BOOLEAN,
  "lang_it" BOOLEAN,
  "lang_es" BOOLEAN,
  "lang_pt" BOOLEAN,
  "city" VARCHAR,
  "locationcity" VARCHAR,
  "locationcitysub" VARCHAR,
  "userinfo_visitdate" TIMESTAMP,
  "countdetails" DOUBLE,
  "crypt" BOOLEAN,
  "flirtstar" BOOLEAN,
  "freshman" BOOLEAN,
  "hasbirthday" BOOLEAN,
  "highlighted" BOOLEAN,
  "distance" DOUBLE,
  "locked" BOOLEAN,
  "mobile" BOOLEAN,
  "online" BOOLEAN,
  "whazzup" VARCHAR,
  "pictureid" VARCHAR,
  "userid" VARCHAR,
  "issystemprofile" BOOLEAN
);