Hinge Data Analysis

Purpose

This project analyzes personal data exported from Hinge to provide valuable insights into the user's experiences on the platform. By examining the user's profile, dating preferences, and interactions with other users, the project aims to reveal patterns, trends, and meaningful statistics that enhance the understanding of how users engage with Hinge and make decisions based on their preferences.

Getting Your Personal Data

Hinge allows active users to request an export of their personal data that were collected while they have had an account. If you have a Hinge account, you can request your data by going to Settings -> Download My Data. It typically takes between 24 and 48 hours to fulfill this request, and once the data are ready, Hinge provides a .zip file with your personal data.

Project Structure

This is the structure of the project and what each section does at a high level.

app/
├── analytics/           # Core application logic and data analysis for users and matches
│   ├── __init__.py
│   └── MatchAnalytics.py
│   └── UserAnalytics.py

├── assets/              # Contains image files copied from the media folder in the export

├── pages/               # Visualization rendering and user interface
│   ├── __init__.py
│   └── HomePage.py
│   └── InfoPage.py
│   └── MatchPage.py
│   └── UserPage.py

├── utilities/           # Helper functions, constants, config, and utilities
│   ├── __init__.py
│   └── DataUtility.py

tests/                   # Unit and integration tests
├── __init__.py
└── analytics 
│   ├── __init__.py
│   └── test_MatchAnalytics.py
│   └── test_UserAnalytics.py

data/                    # Local storage for raw data from the personal export
└── export/ 
│   ├── media/           # Images that were uploaded to Hinge
│   └── matches.json
│   └── user.json
│   └── media.json
│   └── prompts.json
│   └── prompt_feedback.json
│   └── selfie_verification.json

README.md                # Project overview and instructions
requirements.txt         # Python dependencies
.env                     # Environment variables 
Dockerfile               # Dockerfile
docker-compose.yml       # Docker Compose configuration 
LICENSE                  # Project license

Analysis Breakdown

The application is divided into two main sections, each providing distinct insights into the user's Hinge usage.

User Analysis

This section contains insights into how the user's profile is presented, the preferences they've set, and how their interactions shape their experience on the app.

User Uploaded Photos, User Demographics & User Location

These slides show basic user information that was uploaded to Hinge including uploaded photos, demographic information about the user, and information about the user's location.

Example visualization

Profile Information Visibility

Looks at displayed vs. not displayed attributes (ethnicity, religion, workplaces, dating intentions etc.), and helps identify if the user is open vs. private about certain topics.

Example visualization

Comparison Between The User and Their Preferences

This shows potential alignment or misalignment between the users profile and their preferences.

Example visualization

Dating Preferences: Dealbreakers vs Open Choices

This bar chart compares the number of 'dealbreakers' versus 'open' preferences across different dating categories, highlighting which factors are most important or flexible in the user's online dating criteria.

Example visualization

Match Analysis

This section reveals patterns in the user's matching behavior, preferences, and key factors that influence successful connections with potential matches.

Message Count Variability by Month (Last 12 Months)

This box plot shows how the number of messages exchanged per match varies across each month over the past year.

Example visualization

Response Latency between Match and First Message Sent

This graph visualizes the response latency, or the time delay between when a match occurs and when the first message is sent.

Example visualization

Duration of Time Between Match and Remove

This histogram visualizes the duration of a connection and when it was removed or blocked.

Example visualization

Match Duration vs. Message Count

This scatter plot explores the relationship between the number of messages exchanged in a match and the time until the match was removed or blocked.

Example visualization

How to Use

1. Export your data from Hinge
2. Install dependencies
   (Using a virtual environment is recommended)
   pip install -r requirements.txt
3. Create a root level folder named data and copy the export folder from the data export inside the data folder. This should contain:

• media/
• user.json
• match.json
  All of these are utilized by the project.

4. Create a .env file and set environment variables for the following:

• USER_FILE_PATH
• MATCH_FILE_PATH
  Refer to the .env-defaults file for details.

5. The Flask app can be run in two ways:
   1. Running the app locally:
      python app/main.py
   2. Running the app with Docker Compose:
      docker compose build
docker compose up -d