r/place Prediction

This repository is home to our research efforts dealing with the r/place experiment. It currently contains code and data for two different papers we published:

a. The 'r_place_success_prediction' directory contains data and code for the success level prediction model, which was presented at ICWSM 2024 (see citations below). b. The 'r_place_drawing_classifier' directory contains the data and code used for the participation prediction model, which was presented at WWW 2022 (see citation below).

Both projects focus on the Community level prediction. We rely on the r/place social experiment that took place in April 2017. The two full research descriptions and modeling processes are proposed in the following paper:

With Flying Colors: Predicting Community Success in Large-scale Collaborative Campaigns. Abraham Israeli and Oren Tsur In Proceedings of the International AAAI Conference on Web and Social Media. Vol. 18. 2024.

This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign. Abraham Israeli, Alexander Kremiansky, Oren Tsur In Proceedings of the 2022 The Web Conference, WWW 2022.

@inproceedings{israeli2024flying,
  title={With Flying Colors: Predicting Community Success in Large-scale Collaborative Campaigns},
  author={Israeli, Abraham and Tsur, Oren},
  booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
  volume={18},
  pages={691--703},
  year={2024}
}

@article{israeli2022must,
  title={This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign},
  author={Israeli, Abraham and Kremiansky, Alexander and Tsur, Oren},
  journal={arXiv preprint arXiv:2201.05334},
  year={2022}
}

r/place Resources

• Wikipedia Page: A wiki page describing the experiment: https://en.wikipedia.org/wiki/Place_(Reddit)
• Experiment Video: A fast-forward video of the 72-hours timelapse of the experiment: https://www.youtube.com/watch?v=XnRCZK3KjUY
• Subreddit: The r/place subreddit in Reddit: https://www.reddit.com/r/place/

Code

Most of the code that was used to load data and train the models is under two folders:

• data_loaders: A folder that contains all the code we used to load data from various sources.

• r_place_drawing_classifier: A folder that contains mode of the code we userd to train the classification models.

Data

The raw level data is taken from the open Pushift repository (https://files.pushshift.io/reddit/). The annotated data (community level) can be found as as a csv file under the "annotated_data" name.

r/place Over Time

2 Hours after r/place was launched.	7 Hours after r/place was launched.	25 Hours after r/place was launched.	72 Hours after r/place was launched.