https://github.com/lamastex/mep

Project MEP: Meme Evolution programme. A terraformed multi-language library to do statistical experiments in Twitter.
https://github.com/lamastex/mep

delta-lake experiments python r scala terraform twitter twitter-api twitter-schemas

Last synced: about 4 hours ago
JSON representation

Project MEP: Meme Evolution programme. A terraformed multi-language library to do statistical experiments in Twitter.

• Host: GitHub
• URL: https://github.com/lamastex/mep
• Owner: lamastex
• License: apache-2.0
• Created: 2021-03-10T10:33:21.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-10-17T12:07:36.000Z (about 2 years ago)
• Last Synced: 2023-11-12T22:22:01.907Z (12 months ago)
• Topics: delta-lake, experiments, python, r, scala, terraform, twitter, twitter-api, twitter-schemas
• Language: HTML
• Homepage:
• Size: 213 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

Awesome Lists containing this project

README

        
# mep

Project MEP: Meme Evolution programme. A terraformed multi-language library to do statistical experiments in Twitter. 

# mep/sc

Project MEP: Meme Evolution programme. A scala library to do statistical experiments in Twitter. 

See [mep/sc](sc/)

# mep/py

Project MEP: Meme Evolution programme. A python toolkit to do statistical experiments in Twitter. 

See [mep/py](py/)

# mep/r

Project MEP: Meme Evolution programme. An R toolkit to do statistical experiments in Twitter. 

See [mep/r](r/)

# mep/infra

Project MEP: Meme Evolution programme. Infrastructure as Code via [terraform.io](https://www.terraform.io/) 

See [mep/infra/tf](infra/tf/)

# Acknowledgements

This project was supported by Combient Mix AB through summer interships, exjobbs and fellowships at 

The Combient Competence Centre for Data Engineering Sciences, Department of Mathematics, 

Uppsala University, Uppsala, Sweden during:

- 2021 to Johannes Graner, Albert Nilsson, Alfred Lindstrom.

This work was partly supported by a grant from the Swedish Royal Society, a grant from the Wallenberg

AI, Autonomous Systems and Software Program funded by Knut and Alice Wallenberg Foundation, and 

Databricks University Alliance with AWS credits to Raazesh Sainudiin.