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Source code and data for paper "Neutral Bots Probe Political Bias on Social Media" by Chen et al.

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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4750190.svg)](https://doi.org/10.5281/zenodo.4750190)

# Introduction

This repo contains the source code and data for the paper *Neutral Bots Probe Political Bias on Social Media* by Chen et al. ([DOI](https://doi.org/10.1038/s41467-021-25738-6), [preprint](https://arxiv.org/abs/2005.08141)).

Social media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots (we call them *drifters*) on Twitter to probe biases that may emerge from interactions between users, platform mechanisms, and manipulation by inauthentic actors.

## Repo Structure

+ **`bot`** contains the source code of the drifters. Use `cd bot; python drifter_main.py <your drifter screen_name>` to activate the drifters.
+ **`data`** contains the code and intermediate data files for the analyses.
  + `data/hashtag_political_alignment` has the implementation of hashtag embedding.
  + [`data/GenerateDataFiles.ipynb`](/data/GenerateDataFiles.ipynb) generates data files for our analyses.
+ **`database`** contains the script to create a PostgreSQL database for the analyses.
+ **`exps`** contains scripts and a notebook to generate the plots and tables for the paper.
    + [`exps/FinalPaperPlots.ipynb`](/exps/FinalPaperPlots.ipynb) reads the output files generated by `data/GenerateDataFiles.ipynb` to produce the final figures.
    + [`exps/algorithm_bias_estimation.ipynb`](/exps/algorithm_bias_estimation.ipynb) reads the output files generated by `data/GenerateDataFiles.ipynb` and runs statistical tests to estimate the algorithmic bias.
+ **`exps/news_seed_popularity`** contains the scripts and data for the news seed popularity analysis
+ **`metric`** contains the scripts to pre-processes the collected data for further analyses. Please run `metric_job.py` as an example. 
    + `analysis.py` and `metrics.py` compute the hashtag-based and url-based political valence score for each Tweet.
    + `time_series_scores.py` computes the political valence changes across time for all drifters. The notebook `data/GenerateDataFiles.ipynb` calls this script.
    + `generate_networks_for_each_bot.py` builds the ego networks for drifters and computes metrics for the echo chamber analyses. The notebook `data/GenerateDataFiles.ipynb` depends on files generated by this script.
+ **`others`** contains the code to initialize and clean up  the drifters.

## Dependencies

The software in this repository has been tested on a linux machine with Python 3 installed.
Installing Python and the dependencies below might require up to one hour.
Data collection for an experiment similar to the one described in the paper would require several months.
Data processing would typically take a few days.

1. Python 3
2. [Jupyter notebook](https://jupyter.org/) is used in some cases to process and visulize the data.
3. [twurl](https://github.com/twitter/twurl), modified as shown [here](https://github.com/twitter/twurl/issues/10), is used to manage the drifters. First you need to create a Twitter app. Each drifter account must authorize the app. The keys of the app can then be used with  `twurl` to control the drifters. 
4. [chatterbot](https://chatterbot.readthedocs.io/en/stable/) is used when drifters reply to tweets that mention them.
5. [tweepy](https://www.tweepy.org/) is used in analysis code.
6. [psycopg2](https://pypi.org/project/psycopg2/) is the database driver.
7. [botometer client library](https://github.com/IUNetSci/botometer-python) is used in conjunction with the [Botometer Pro API](https://botometer.iuni.iu.edu/#!/api) to get data from the Twitter API and then calculate bot scores for friends and followers of the drifters.
8. [gensim](https://pypi.org/project/gensim/3.5.0/) provides an implementation of the word2vec algorithm for calculating the political alignment of the hashtags.

## Citation

You may cite our preprint as:

```bib
@article{chen2021neutral,
  title={Neutral bots probe political bias on social media},
  author={Chen, Wen and Pacheco, Diogo and Yang, Kai-Cheng and Menczer, Filippo},
  journal={Nature communications},
  volume={12},
  number={1},
  pages={1--10},
  year={2021},
  publisher={Nature Publishing Group}
}

```

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