Session 1: Introduction to the course and digital research methods
In this session, we will first go through an overview of key digital research methods, criteria for comparing their strengths and weaknesses, and the research journey at large with examples of major challenges when using digital research methods.

There is a mass of digital platforms available, some limit their users to a particular software and hardware, others are open and can be combined with others. In this Introduction part, the students will get a non-exhaustive list of platforms, software and appliances. We will discuss and reflect their usability and possible applications for research projects.

Compulsory reading for session 1: see reading list

Session 2: Online Community Research: Research on “networked public spheres”
Online communities are particularly fruitful research contexts as they allow insight into naturally occurring large-scale online conversations surrounding a wide range of topics. Collecting, structuring, and analyzing such conversations allows researchers, among others, to understand which topics are being discussed, how they develop over time, which actors are driving a conversation, which viewpoints are (un)popular or controversial in a community and which sentiment a specific conversation or response may elicit. During this session, we will discuss how to collect as well as meaningfully structure and analyze large-scale conversation data. Furthermore, we will weigh potential implications and ethical boundaries of those methods.

Compulsory reading for session 2: see reading list

Session 3: Computational communication research: Machine learning for automated content analysis
Texts, audio, and images are used to persuade people and critical in media framing, agenda-setting, and propaganda. Traditionally, researchers adopt content analysis to study text, audio, and image data. One of the most obvious drawbacks of content analysis is the time, effort, and money needed to complete a reliable and valid study. Automated content analysis was developed and advanced by machine learning.

To equip students with advanced computational communication research skills, this session will briefly introduce the concept of machine learning (classification) and its application. In particular, we are interested in using machine learning for automated content analysis. This session will offer an in-depth overview of automated methods for large-scale text, audio and image analysis and explains their usage and implementation. Furthermore, we will weigh potential implications and ethical boundaries of those methods.

Compulsory reading for session 3: see reading list

Session 4: Digital ethnography and humanistic text-mining
With computational social science and digital humanities, large volumes of texts have become available as primary sources for empirical analysis. To research this material, digital ethnography has developed as a distinct methodology. Digital ethnography requires immersion in the online life of people, statements, and objects over an extended period. The research process is data driven. The field is read as a text and findings are communicated as factual narratives. The researcher may be an active participant or observe from the outside but is always a medium through which information passes.

In this session, students will be introduced to digital text-mining tools for understanding narrative conventions and discursive structures. Furthermore, we will weigh potential implications and ethical boundaries of those methods, particularly on the problem of the researcher’s positioning. In lieu of the extended period of study, historical sources will be introduced.

Compulsory reading for session 4: see reading list

Session 5:  Digitalizing experiments. Experimenting with wearable as research tools 
In the increasing digitalized world people interact with devices and AI for most of their daily tasks. The study of communication behavior is shifting towards digital environments, capturing human-machine interactions, and integrating the use of wearables. This also allows for interesting opportunities to integrate digital tools into experimental designs and methods. Researchers have done so in various ways, by using virtual reality for interventions and measurement of biases, by using eye tracking, or by immersing the participants even more into the digital setting and using wearables to track outcomes. We will discuss the methods and techniques, as well as the potential implications and ethical boundaries of those methods.

Compulsory reading for session 5: see reading list

Session 6: Summary and evaluation
In this session, we will discuss and reflection on our learning through round-table dialogue and presentations of potential applications on research, peer-review session of term paper outlines as the final learning activity.