With the plethora of data available in digital spaces, it only makes sense that scholars want to access, visualize, and analyze this data. Many software packages are available to aid in this pursuit, and programming languages paired with social media APIs make the scraping process even more customized. This guide will focus on scraping, visualizing, and analyzing Twitter data.
Originally created by: Angela Cirucci
Currently updated & maintained by: Elizabeth Rodrigues
Some tips before you get started:
Twitter is a micro-blogging site where users can broadcast status updates of 140 characters or less. If you aren't that familiar with the site, you can explore it here.
While there are many social networking sites that hold rich information for research, Twitter is an ideal space because:
Before getting started witih your research, you want to be sure that your research question matches the types of research best served by Twitter data. Social media scraping, in general, is best utilized when you are trying to understand some phenomenon that is taking place online.
In particular, Twitter data allows you to:
Scraping is only the first step. Once you have your data, there will be much cleaning and organizing to do.
Mining the site only gives you raw information. Some scraping software packages come with visualization and analysis tools. You can also employ other methods and tools.
Some ways of visualizing and analyzing your data include: comparing to other online norms, comparing to other social networking site performances, and comparing to offline phenomena. For example, you may want to compare networks with quantitative methods, or you may want to compare the content of tweets from two different hashtags with qualitative methods.
The rest of this guide will introduce you to some of my preferred tools and methods for scraping, visualizing, and analyzing Twitter data.