Replication Data for: A Computational Analysis of Social Media Scholarship

Dramatic increases in large-scale data generated through social media, combined with increased computational power, have enabled the growth of computational approaches to social media research, and social science in general. While many of these approaches require statistical or computational training, they have the great benefit of being inherently transparent—allowing for research that others can reproduce and learn from. To that end, we wrote a book chapter in the Sage Handbook of Social Media in which we obtain a large-scale dataset of metadata about social media research papers which we analyze using a few commonly-used computational methods. This repository provides the code, data, and documentation designed to tell you exactly how we did that and to walk you through how to reproduce our results and our paper by running the code we wrote. You can find the chapter here: Foote, Jeremy D., Aaron Shaw, and Benjamin Mako Hill. 2017. “A Computational Analysis of Social Media Scholarship.” In The SAGE Handbook of Social Media, edited by Jean Burgess, Alice Marwick, and Thomas Poell, 111–34. London, UK: SAGE. [Official Link] [Preprint PDF] Documentation on how to download and use these data are provided on the following website: https://communitydata.science/social-media-chapter/ A copy of our documentation website can be found in the files README.md and README.html included in this repository.