Automated Collection of Web and Social Data

Citizens across the globe spend an increasing proportion of their daily lives online. Their activities leave behind granular, time-stamped footprints of human behavior and personal interactions that represent a new and exciting source of data to study standing questions about political and social behavior. This course will provide participants with an overview of the new sources of data that are now available, and the computational tools required to collect the data, clean it, and organize it in a format amenable to statistical analysis.

The course is structured in three blocks. The first part (Days 1 and 2) will offer an introduction to the course and then dive into the basics of webscraping; that is, how to automatically collect data from the web. This session will demonstrate the different scenarios for webscraping: when data is in table format (e.g. Wikipedia tables or election results), when it is in an unstructured format (e.g. across multiple parts of a website), and when it is behind a web form (e.g. querying online databases). The tools available in R to achieve these goals – the rvest and RSelenium packages – will be introduced in the context of applied examples in the social sciences. Students are also encouraged to come to class with examples from their own research.

NGOs, public institutions, and social media companies increasingly rely on Application Programming Interfaces (API) to give researchers and web developers access to their data. The second part of the course (Day 3) will focus on how we can develop our own set of structured http requests to query an API. We will discuss the components of an API request and how to build our own authenticated queries using the httr package in R. We will apply these skills to two examples: the New York Times API (to query newspaper articles) and the Clarifai API (to automatically tag visual content with machine learning).

The third part (Days 4 and 5) will teach how to collect and analyze data from social media sites. We will begin with an overview of the research opportunities and challenges of using social media data in the social sciences. We will then discuss the data available through Twitter’s REST and Streaming API. As part of the guided coding exercises, we will learn how to collect tweets filtered by keywords, location, and language in real time using different R packages; and how to analyze the data to find the most mentioned hashtags and users and to map the location of the tweets. Our last session will demonstrate how to scrape public Facebook pages through the Graph API using the Rfacebook package. As an illustration of how to analyze tweets and Facebook posts collected with these methods, we will use a dictionary method to characterize politicians’ rhetoric on social media.

An underappreciated part of the research process is data manipulation – it is rarely the case that the dataset we want to use in a study is available in a format suitable for analysis. Data “munging” is tedious, but there are ways to make it more efficient and replicable. Our last session will also discuss some good practices to clean and rearrange data obtained from the web.

The course will follow a "learning-by-doing" approach. Code and data for all the applications will be provided, and students are encouraged to bring their own laptops to follow along. Each session will start with around 45-60 minutes of lecturing and coding led by the instructor, followed by "data challenges" where participants will need to apply what they have learned to new datasets. Although these data challenges will be started in class, participants will be asked to complete them after the end of each session. Solutions for each challenge will be posted by the beginning of the following class, and we will leave time for questions.

After the course, students will have an advanced understanding of the web and social data available for social science research and will be equipped with the technical skills necessary to collect and clean such datasets on their own.

The course will assume intermediate familiarity with the R statistical programming language. Participants should be able to know how to read datasets in R, work with vectors and data frames, and run basic statistical analyses, such as linear regression. More advanced knowledge of statistical computing, such as writing functions and loops, is helpful but not required.