Using Digital Trace Data in the Social Sciences, University of Konstanz (Summer 2018)

Instructor: Andreas Jungherr

Week 1: Introduction and Conceptual Issues in the Use of Digital Trace Data in Social Science

Welcome to the course! You have interesting sessions to look forward to. At the end of which, I hope you are at least as excited by the work with digital trace data as you are now but of course much more able to translate that excitement into actual scientific projects.

In our first session, we will discuss the background of working with digital trace data. We will start by discussing some of the expectations connected with this new data sources. Here, we will discuss the terms Computational Social Science, Digital Methods, Big Data, and Digital Trace Data.

We then will focus on two prominent fallacies in the work with digital trace data:

  1. The n = all fallacy;
  2. The mirror hypothesis.

Both fallacies can be found explicitly or implicitly in prominent works based on digital trace data. They are central to limiting the value of research based on digital trace data and to raising false expectations of which types of insight these data type can actually deliver.

Central to avoiding these fallacies are three often neglected steps:

In this context, we will quickly talk about the value of interpreting digital trace data as mediated traces of user behavior and, therefore, mediated reflections of social or political phenomena of interest.

After this, we will close by discussing a series of interesting questions in political science closely related to the data generating process leading to the publication of tweets and, therefore, closely connected with digital trace data.

Required Readings:

Background Readings:

Week 2