Code for the paper Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media.
- To get started, create a Conda environment:
conda create -n twitter python=3.9.11 - Then, activate the Conda environment:
conda activate twitter - Finally, install all requirements:
pip install -r requirements.txt
To request access to our dataset, you can use this form.
Descriptions of the data files can be found in DATA.md.
effects.ipynb: Average treatment effect (ATE) graph (Figure 1) and political tweets by in-group and out-group (Figure 2)demographics.ipynb: User demographics and ANES data comparison (SM section S2)metadata_stats.ipynb: Descriptive statistics for tweet metadata and individual user-level amplification (SM sections S4.1 & S4.2)likert_distributions.ipynb: Distribution of responses to Likert survey questions (SM section S4.3)outcomes_by_rank.ipynb: Outcomes broken down by the position of tweet in each timeline (SM section S4.4)effects_by_tweet_threshold.ipynb: ATE calculations across varied tweet thresholds (SM section S4.5)gpt_judgements.ipynb: ATEs for tweet outcomes calculated with GPT-4 labels (SM section S4.6)effects_het.ipynb: Effects calculated when restricting to subpopulations of users (SM section S4.7)
If you use our code or data, please cite our paper:
@article{milli2023twitter,
title={Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media},
author={Milli, Smitha and Carroll, Micah and Wang, Yike and Pandey, Sashrika and Zhao, Sebastian and Dragan, Anca D.},
journal={arXiv preprint arXiv:2305.16941},
year={2023}
}