Replication Code for U.S. 2020 Facebook and Instagram Election Study
2023
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Citation
Description
This record is for replication code for datasets in the U.S. 2020 Facebook and Instagram Election Study. Code will be provided along with related datasets in a secure virtual data enclave, upon approval of a Restricted Data Application.
Details
Title
Replication Code for U.S. 2020 Facebook and Instagram Election Study
Creator
Meta Platforms, Inc. Producer
Issued Date
2023-07-27
Version
v1
Alternate Identifiers
Status
Published
Access Rights
This software has one level of access: Virtual Data Enclave.
- Virtual Data Enclave files have access restrictions due to the sensitivity of the data or data sharing requirements. These data require an application. Once your application is approved, researchers will access and use the data inside ICPSR’s secure computing environment, called our Virtual Data Enclave (VDE). Click the “Apply for Access” button below to apply through our application portal.
Funding Information
Meta Platforms, Inc.
Citation
Meta Platforms, Inc. Replication Code for U.S. 2020 Facebook and Instagram Election Study. Inter-university Consortium for Political and Social Research [distributor], 2023-07-27. https://doi.org/10.3886/spb3-g558
Record Appears in
Additional Notes
The U.S. 2020 Facebook and Instagram Election Study (US 2020 FIES) is a partnership between Meta and academic researchers to understand the impact of Facebook and Instagram on key political attitudes and behaviors during the US 2020 election.
Software Description
The data in this study was analyzed using R (version 4.1.1), which was executed via R notebooks on JupyterLab (3.2.3). The analysis code imports several R packages available on CRAN, including dplyr (1.0.10), ggplot2 (3.4.0), xtable (1.8-4), aws.s3 (0.3.22), glmnet (4.1.2), and estimatr (1.0.0). We also developed a custom R package to read, process, and analyze data that will be included in the software package.
Software Version
v1
Programming Language
R
Environment and Dependencies
dplyr (1.0.10), ggplot2 (3.4.0), xtable (1.8-4), aws.s3 (0.3.22), glmnet (4.1.2), and estimatr (1.0.0)