Network Analysis of Hispanic streamers in the Twitch community

Analysis of Twitch streamers in the Hispanic community based on their follows. We used the Twitch API to get information about users, their followers, and who they follow in order to construct the dataset we used to carry our analysis.

The main objectives were the following:

• Obtain insights and Visualize in detail the Twitch Hispanic Network

• Perform Community detection analysis on the graph of streamers.

• Rank streamers using network metrics

• Make a simple recommendation system for Twitch streamers based on their follows.

Authors:

• Simón E. Sánchez Viloria
• Andres Ruiz Calvo
• David Méndez Encinas
• Enrique Botía Barbera

Streamlit Dashboard

A dashboard showing the insights of our analysis was made with streamlit. You can go here to see it live or clone this repository, install the dependencies and run streamlit run app/main/main.py to execute it locally yourself. Also, here you can see all the images we obtained in our graph analysis.

Usage and Reproducibility

In order to reproduce the Analysis we made on this project you can follow the steps below:

1. Clone this repository.
2. Download the dependencies with pip install -r requirements.txt
3. You can use the dataset we built for this project that is located in the data/ directory or create one yourself with the command python -m src.data --root_user {YOUR SELECTED STREAMER} --output_file "data/streamers.feather" --max_users 10000
4. Go though our notebooks and source code and run the experiments and analysis we made for yourself.
5. Run the streamlit app with streamlit run app/main/main.py to visualize the results in a dashboard like the one shown in the image above.

Note: If you want to make your own dataset you will need to obtain your own keys to the Twitch API from Twitch and set them up in your environment variables before running the code.

export TWITCH_CLIENT_ID=<your twitch client id>
export TWITCH_CLIENT_SECRET=<your twitch client secret>

References and Credit

• Twitch API: https://dev.twitch.tv/docs/api/reference/

• Ana Blanco's (casiopa) EDA-IMDb streamlit app.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│ 
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├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
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├── app               <- Streamlit app (dashboard)
│
└── Dockerfile         <- Dockerfile to build the docker image of the streamlit app used in this project.

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Project based on the cookiecutter data science project template. #cookiecutterdatascience