Suspension of prominent accounts minimally impacts follower engagement

Kayla Duskin (1, 2), Jevin West (1,2) and Joseph B. Bak-Coleman(3)

1. University of Washington Center for an Informed Public
2. eScience Institute
3. Craig Newmark Center, Columbia University}]

Repository information

This repository provides all code and data necessary to generates results, tables, and figures found in the article "Suspension of prominent accounts minimally impacts follower engagements",

Article abstract

The rise of problematic information online, including misinformation, is concerning to the public, social media companies, and governing bodies alike. As one form of intervention, social media platforms may temporarily or permanently suspended accounts that spread misinformation, at the risk of losing traffic vital to platform revenue and running head on into debates around free speech. Here we examine the impact on platform engagement following removal of six prominent accounts form Twitter during the COVID-19 pandemic. Focused on users who engaged with the removed accounts, we find that suspension did not meaningfully reduce activity on the platform. Moreover, we find that removal of the prominent accounts minimally impacted the diversity of information sources consumed.

License and citation information

If you plan on using this code for any purpose, please see the license and please cite our work as below:

Kayla Duskin, Jevin West, and Joseph B. Bak-Coleman. Suspension of prominent accounts minimally impacts follower engagement. Preprint (October 2023). https://osf.io/preprints/socarxiv/x4jau/

Directories

• IdentifyAmplifiers.ipynb : identify the highly engaged and moderately engaged users from internal database

• CollectAmplifierTweets.ipynb : gather tweets from internal database and query twitter api

• Analysis.ipynb : run the model, create figures

• src: models (*.stan), code used to clean the raw data, code for generating figures, and utilities used in the primary analysis.

  • DBtools.py helper functions for connecting to postgres database
  • data_processing.py processing data to get daily counts used in model
  • fetch_user_tweets.py code for fetching user tweets from Twitter academic API
  • inequality_measurements.py calculate shannon diversity
  • model.py Helper functions for running the main gaussian process model.
  • *.stan Stan model code
  • plotting.py Code for creating figures
  • util.py Miscellaneous utilities.

• data: data files in comma-separated values (.csv) formats

  • ./: raw data files

• out: output files (generated by running)

  • out/figures: Figures generated from results
  • out/posteriors: Posterior objects for each follower saved as .json files
  • out/split_data: The larger dataset split into individual followers for ease of processing
  • processing_df: DF used to keep track of followers, conditions, and processing status
  • changes.csv: Computed changes in activity across removed users and conditions
  • changes_grouped.csv: Changes.csv grouped by outcome, group (med, top), and suspended user

Reproducing analysis

You can reproduce the analysis of the aggregated date, including all figures and tables by following the guide below. Please note that minor, non-qualitative differences may exist due to difference in pseudorandom number generation.

Getting the code

First download this repository. Either download directly or open a command line and type:

git clone https://github.com/kduskin/TwitterSuspendedUsers