Analysing digital data: Text, networks, and visuals

Introduction

Key terms:digital/computational methods; analysis of texts, networks, and visuals; Gephi and R (and other software); introductory course, relevant for students in any phase of the PhD trajectory.

Many research disciplines have a vested interest in digital data. Such data consist of information that we create, share, or leave behind when using digital platforms and services. Examples include social media posts and comments, news articles, website content, images and videos, as well as likes, views, and online connections between people.

This course introduces practical methods for collecting, preparing and analysing digital data, with a focus on textual, network and visual analysis. 

The emphasis is on developing digital research skills rather than mastering a specific software package. By the end of the course, participants will understand the principles behind common digital methods and they will be able to apply these skills to a wide range of data sources.

The course provides an accessible introduction to computational approaches and does not require prior programming experience. Rather than teaching extensive coding or machine learning techniques, it introduces participants to the logic of these methods and demonstrates how existing software can support their research.

The course combines short lectures with hands-on exercises. Participants are expected to prepare for each session by completing readings, watching videos and preparing assignments.

Course information

ECTS: 2.5
Number of sessions: 4
Hours of session: 3

Who is this for?

Relations with other courses

Compared with the EGSH courses Data Literacy through R: Managing and Analysing Data Responsibly and Programming with Python for Researchers, this course places less emphasis on programming and more on developing practical digital research skills through a combination of open-source tools and introductory coding examples. 

Unlike the SICSS-ODISSEI Summer School on Computational Social Science, which focuses on machine learning, causal inference and intensive computational workflows, this course provides an introductory overview of digital data collection, text analysis, network analysis and visual analysis. 

The course also complements qualitative methods courses such as Visual Ethnography by introducing computational approaches to analysing visual and multimodal data.

Sessions and preparations

Session 1: Collecting digital data
This session introduces different forms of digital data and discusses opportunities and challenges of using online data in research. Participants learn how to collect data from sources such as social media platforms, news and websites using API’s and web scraping. The session also covers basic data cleaning and preparation. 
Preparation: Complete the pre-class survey and assigned readings, install the required software and review the course syllabus.

Session 2: Analysing text
This session introduces computational approaches to analysis of textual data. Participants learn how to preprocess textual data and apply methods such as frequency analysis, word co-occurrences, topic modelling and sentiment analysis. 
Preparation: Complete the assigned readings and assignment.

Session 3: Analysing networks
This session focuses on network analysis as a method for examining relationships between online content, individuals or interactions. Participants learn the principles of network analysis and visualisation and use Gephi to visualise, explore and interpret networks derived from digital data.
Preparation: Complete the assigned readings and assignment.

Session 4: Analysing visual and multimodal data
This session introduces methods for analysing images, videos and other multimodal forms of digital communication. Participants explore approaches to automated- and computational forms of analysing visual- and multimodal data. 
Preparation: Complete the assigned readings and assignment.