Notes on (Computational) Social Media Research - Supplements

This repository contains Jupyter notebooks and resources for the work-in-progress textbook on computational social media analysis. The notebooks will cover various topics related to social media data analysis, natural language processing, and machine learning.

Table of Contents

• Project Overview
• Notebooks
• Datasets
• Getting Started
• Contributing
• License
• Contact

Project Overview

Notes on (Computational) Social Media Research is a work-in-progress website being developed by Michael Achmann as part of his Ph.D. research and teaching at the Chair for Media Informatics, University of Regensburg. The textbook aims to provide comprehensive guidance on computational social media analysis, exploring various methodologies, techniques, and tools for social media research and will accompany the research seminar "Computational Analysis of Visual Social Media" in the 2023/24 winter semester.

Additional tutorials are available on Medium, the corresponding notebooks are hosted in the ig-tutorial repository.

Notebooks

As the project is still in progress, the notebooks will be added to the notebooks/directory gradually. Stay tuned for updates!

Datasets

The repository may also include datasets for use with the notebooks. If applicable, datasets will be made available in the datasets/ directory.

Getting Started

To use the notebooks in this repository, you have two options:

1. Local Installation (Jupyter Notebook)

You can run the Jupyter notebooks locally on your computer. To do this, you will need to have Jupyter Notebook installed. If you don't have it installed, you can follow the installation instructions on the Jupyter website. Once you have Jupyter Notebook installed, you can clone this repository using the following command:

bash git clone https://github.com/michaelachmann/social-media-lab.git

Then, navigate to the repository directory:

bash cd social-media-lab

Start the Jupyter Notebook server:

bash jupyter notebook

Open any notebook file (e.g., example_notebook.ipynb) to begin exploring social media analysis techniques.

2. Google Colab

If you prefer to use Google Colab, you can run the notebooks directly from your web browser. Google Colab is a free cloud-based platform that provides access to Jupyter notebooks with integrated GPU support. It allows you to run code, including Python and TensorFlow, on Google's servers without any local installation.

To run a notebook in Google Colab, simply click on the "Open in Colab" badge inside the notebook. This will open the notebook in Google Colab, where you can execute the code and analyze social media data directly in the cloud.

Please note that while Google Colab is convenient for quick experimentation, you might need to sign in with your Google account and save a copy of the notebook to your Google Drive for long-term storage.

Contributing

We welcome contributions to the project. If you have ideas, improvements, or would like to add new notebooks or datasets, please open an issue or submit a pull request. For significant contributions, please first discuss the changes in the issues section.

License

The content of this project, including the Jupyter notebooks and other resources, is licensed under the GNU General Public License version 3.0 (GPL-3.0). For more details, see the LICENSE.md file.

Citation

Please use one of the following options for citing the content of this repository when using the notebooks and examples in your academic work.

Michael Achmann. (2023). michaelachmann/social-media-lab: DOI Release (v0.0.1). Zenodo. https://doi.org/10.5281/zenodo.8199902

bibtex @software{michael_achmann_2023_8199902, author = {Michael Achmann}, title = {michaelachmann/social-media-lab: DOI Release}, month = jul, year = 2023, publisher = {Zenodo}, version = {v0.0.1}, doi = {10.5281/zenodo.8199902}, url = {https://doi.org/10.5281/zenodo.8199902} }

Contact

If you have any questions or need further assistance, you can reach Michael Achmann at:

• Website: Michael Achmann
• Email: michael.achmann@informatik.uni-regensburg.de