# Facebook as an Avenue to News: A Comparison and Validation of Approaches to Identify Facebook Referrals ### *Anonymized* The data and code provided here enable the replication of the paper ***Facebook as an Avenue to News: A Comparison and Validation of Approaches to Identify Facebook Referrals***. The archive contains R scripts and data files. ## R scripts | script | description| |---|--| | `01_load_data.R` | Script for loading the data | | `02_Validation_of_Approaches.R` | Script that reproduces the results reported in the main paper (Figure 1) | | `03_Analysis_Prevalence.R` | Script that reproduces the results reported in the main paper (Figure 2) | | `04_Distribution_News_Types.R` | Script that reproduces the results reported in the main paper (Figure 3) | | `05_Correlates.R` | Script that reproduces the results reported in the main paper (Figure 4) | | `06_Diversity.R` | Script that reproduces the results reported in the main paper (Figure 5) | | `Appendix_Correlates_Contrasts.R` | Script that reproduces the results reported in the online appendix (Figure S3) | | `Appendix_Diversity_Bootstrap.R` | Script that reproduces the results reported in the online appendix (Figure S4) | ## Data files Aggregated data to reproduce the results of the main paper are provided in the folder `Data`. The data files are read into the R scripts where appropriate. ## Web tracking data collection We utilized a web tracking tool developed by academics (Aigenseer et al., 2019). The web tracking tool is currently undergoing the process of becoming open source. A link to a GitHub repository will be available shortly. The raw web tracking data cannot be shared in order to protect the privacy of study participants and due to proprietary constraints. Instead, we share aggregated data files that enable the replication of the analysis. ## References Aigenseer, V., Urman, A., Christner, C., Maier, M., Adam, S., Makhortykh, M., & Gil-Lopez, T. (2019, November 24). Webtrack–desktop extension for tracking users’ browsing behaviour using screen-scraping. GESIS Computational Social Science (CSS) Seminar, Köln. https://boris.unibe.ch/id/eprint/139219