Introduction to Digital Humanities and Social Analytics

Vrije Universiteit Amsterdam

Course Description

  • Course Name

    Introduction to Digital Humanities and Social Analytics

  • Host University

    Vrije Universiteit Amsterdam

  • Location

    Amsterdam, The Netherlands

  • Area of Study

    Communication Studies, Media Studies

  • Language Level

    Taught In English

  • Course Level Recommendations


    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    At the end of this course the student
    • Has a global idea of recent developments in the field of data digitization and research in the fields of digital humanities and social analytics
    • Understands the complexity and challenges of (global) data developments
    • Understands the relevance of data-oriented research for humanities and social sciences
    • Is able to critically evaluate the use of digital data in humanities and social science research and to reflect on the implications of the selection, structuring and manipulation of data for the outcome of their own work
    • Has a basic knowledge of data formats and ontologies
    • Is able to apply various computational techniques for cleaning, parsing and structuring / modelling of digital data
    • Is aware of disciplinary differences among students of humanities, social science, informatics and other academic fields and is equipped to work in multi-disciplinary teams

    This course consists of three modules:
    1. Study of current developments in the digital humanities and social analytics through reading, evaluation and discussion
    2. Introduction to hermeneutics, data criticism and tool criticism.
    3. Practice in working with structured data, data curation and modelling
    The humanities and social sciences have more and more digital material at their disposal. Increasingly literature, newspapers, archival sources as well as library and museum catalogues become available in digital formats. Meanwhile digital born data from social media, news media government bodies and all sorts of institutions allow scholars to work with enormous amounts of new data on human behavior and communication. How can humanities researchers and social scientists use digital data to support their research? What are the digital tools at their disposal and how can these tools provide new perspectives and research questions? In this course you will be introduced to this cross-disciplinary research field, to the data collections, computational tools and methods used. In class we will also discuss what is really new about digital humanities and social analytics and evaluate both the promises and the limits of some digital methods.
    Hermeneutics is the theory of interpretation. We will discuss hermeneutics in relation to source-criticism and evaluate what the methodological and theoretical implications are of the use of digitized data, quantitative methods and large datasets.
    A first step in data-oriented research is a critical understanding of the providence, characteristics, shape and limits as well as the potential of a given dataset. In this course, students will familiarize the ‘research data lifecycle’: Starting with the critical analysis of how data are generated or how they are created through digitization of original sources (objects), how data are formatted and structured, how they can be cleaned and annotated, how they can be modelled and analyzed, and finally documented, stored and published. Practical choices that are to be made in the course of this process have crucial implications for the way data can be used in research. In class we will discuss the use of ontologies and different data formats and data models. Practical problems such as the heterogeneity of humanities and social media data, incompleteness, disambiguation, partiality and bias will be discussed as well.

    Lectures, excursions, discussion, interdisciplinary group work and hands-on practicals

    Practical group assignments (40%) and written exam (60%).

Course Disclaimer

Courses and course hours of instruction are subject to change.

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