AANA18119U  Digital Methods: From Facebook Ethnography to Computational Social Science

Volume 2019/2020
Content

The rise of new types of digital data and the varieties of social life taking place on social media platforms enable new relations between quantitative and qualitative methods of inquiry and analysis. How such new complementarities are best exploited for social-scientific and practical purposes will be the focus of this course.

The course will offer students the opportunity to learn digital methods across the qualitative and quantitative spectrum, and to experiment with how these complement one another. The course will be structured around a mini research project, which takes students through several stages: from research design, data collection, data cleaning, to analysis. Methodologically, students will learn a wide range of methods and skills for conducting digital and computational research.

First, the course will take the form of a python programming boot camp where students will learn the python programming necessary for being efficient and flexible in working with digital data. We will learn basic the syntax, data types and structures in python and how to manipulate and work with tabular, text and network data. The boot camp will also introduce to various python libraries that will be used throughout the course hereunder numpy, pandas, networkx, scikit-learn and scipy. This first part of the course will take up one third of the course. 

Second, the course will introduce students to the fundamentals of digital ethnography on social media platforms and other online spaces (e.g. Facebook groups, Twitter hashtags, Reddit threads, intranets, discussion fora, etc.). Digital ethnography involves conducting participant observation and interviews in digital spaces for the purpose of learning the dynamics of a particular online social setting. Digital ethnography thus provides the interpretative grounding which ensures that the categories and social processes that will be quantitatively assessed through digital and computational methods later in the course, are grounded in the meaning-making practices of the actors themselves.

Third, students will learn how to scrape, clean and do visualizations, network analysis, clustering and multidimensional scaling in and with python. These techniques will be used to explore, map out and visualize the varying densities and differences in data for the purpose of qualitatively exploring patterns in and across online social settings.

Fourth, students will learn how use computational text analysis, hereunder supervised machine learning, to quantify aspects of their qualitative inquiry. The course will go through more classical issues in content analysis around the construction of coding schemes and various forms of validity issues relevant for working with textual data. The course will also work through how to train models to automatically label textual data in ways that are sensitive to biases and limitations to of automated text analysis. Finally, the course will run through various simple analytical strategies to analysis the labeled data.

Students will first be tasked with finding a project idea, and to invent a research design that combines qualitative and quantitative digital methods around an online case study. Following this, students will be tasked with collecting data through ethnography, digital methods and computational social science programming. Students will then be tasked to conduct an analysis of the case study, while reflecting on the methodological, epistemological and practical aspects of combining heterogeneous datasets and methods.

 

Learning Outcome

Skills
• Use mixed methods research strategies to produce a case study analysis of an online social phenomenon.
• Master a set of methods, tools and skills for digital research (digital ethnography; network visualization; topic modelling; and supervised machine learning)

Knowledge
• Understand and reflect on mixing qualitative and quantitative modes of inquiry, specifically how ethnography and computational social science datasets can be combined.
• Evaluate pitfalls and potentials of using large scale digital data for understanding social action vis a vis “thick” ethnographic data.

Competences
• Students will learn how plan and carry out a mixed-methods research project using digital methods and hetereogenous forms of data from start to finish.
• Student will be able to handle both the qualitative and quantitative challenges of working with data from online sources.

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Lectures
Seminars
Coding and programming boot camp
Exercises
Project-based work
Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
Credit
15 ECTS
Type of assessment
Written assignment
The exam will consist of an essay (a project paper) analyzing an online social phenomenon or tendency (e.g. the dynamics of a Facebook network; topics on discussion fora; a political controversy, etc) that the students will choose themselves. The exam can be written and submitted individually or in groups. As the course is based on group work in-class, we encourage students to write and submit their exams in groups of 3-4 members. The exam must be answered in English. The plagiarism rules must be complied to and students must follow the existing rules regarding co-written assignments.

The total length of the exam must not exceed 30,000 keystrokes for a single student. For groups of two students the maximum is 40,000 keystrokes. For groups of three students the maximum is 45,000 keystrokes and for groups of four students the maximum is 50,000 keystrokes.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Re-exam

1. re-exam:

A new essay with a revised problem statement must be submitted at the announced date. The students must sign up for the 1. re-exam. 

2. re-exam:

A new essay with a revised problem statement must be submitted at the announced date next semester. The students must sign up for the 2. re-exam.

Criteria for exam assesment

See descriptions of learning outcome. Formalities for Written Works must be fulfilled, read more: MSc Students/ BA students (in Danish)/ exchange and credit

  • Category
  • Hours
  • Lectures
  • 42
  • Seminar
  • 42
  • Exam
  • 73
  • Preparation
  • 207
  • Project work
  • 50
  • Total
  • 414