Introducing Computational Methods to Social Media Scientists

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The ubiquity of large-scale data and improvements in computational hardware and algorithms have provided enabled researchers to apply computational approaches to the study of human behavior. One of the richest contexts for this kind of work is social media datasets like Facebook, Twitter, and Reddit.

We were invited by Jean BurgessAlice Marwick, and Thomas Poell to write a chapter about computational methods for the SAGE Handbook of Social Media. (You can read the book chapter here. ) Rather than simply listing what sorts of computational research has been done with social media data, we decided to use the chapter to both introduce a few computational methods and to use those methods in order to analyze the field of social media research.

Hairball graphic

A “hairball” diagram from the chapter illustrating how research on social media clusters into distinct citation network neighborhoods.

Explanations and Examples

In the chapter, we start by describing the process of obtaining data from web APIs and use as a case study our process for obtaining bibliographic data about social media publications from Elsevier’s Scopus API.  We follow this same strategy in discussing social network analysis, topic modeling, and prediction. For each, we discuss some of the benefits and drawbacks of the approach and then provide an example analysis using the bibliographic data.

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This article has been reposted from Benjamin Mako Hill’s Copyrighteous blog which distributes its content as free cultural work under the Creative Commons Attribution-ShareAlike license. The book chapter and this blog post were written with Jeremy Foote and Aaron Shaw.

We think that our analyses provide some interesting insight into the emerging field of social media research. For example, we found that social network analysis and computer science drove much of the early research, while recently consumer analysis and health research have become more prominent.

More importantly though, we hope that the chapter provides an accessible introduction to computational social science and encourages more social scientists to incorporate computational methods in their work, either by gaining computational skills themselves or by partnering with more technical colleagues. While there are dangers and downsides (some of which we discuss in the chapter), we see the use of computational tools as one of the most important and exciting developments in the social sciences.

Steal this paper!

One of the great benefits of computational methods is their transparency and their reproducibility. The entire process—from data collection to data processing to data analysis—can often be made accessible to others. This has both scientific benefits and pedagogical benefits.

To aid in the training of new computational social scientists, and as an example of the benefits of transparency, we worked to make our chapter pedagogically reproducible. We have created a permanent website for the chapter at and uploaded all the code, data, and material we used to produce the paper itself to an archive in the Harvard Dataverse.

Through our website, you can download all of the raw data that we used to create the paper, together with code and instructions for how to obtain, clean, process, and analyze the data. Our website walks through what we have found to be an efficient and useful workflow for doing computational research on large datasets. This workflow even includes the paper itself, which is written using LaTeX + knitr. These tools let changes to data or code propagate through the entire workflow and be reflected automatically in the paper itself.

If you  use our chapter for teaching about computational methods—or if you find bugs or errors in our work—please let us know! We want this chapter to be a useful resource, will happily consider any changes, and have even created a git repository to help with managing these changes!

This blog post was originally published on the Community Data Science Collective blog.

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