Methods of text analysis in humanities and social pt. 2 1600-SZD-ID-MAT2
The workshop responds to the challenges posed by the ongoing process of digitisation, the exponential growth of textual data, especially
so-called unstructured data, and the development of software and scientific infrastructures that open up new possibilities for in-depth
textual analysis. The individual courses will introduce broadly defined methods that correspond to selected research strands and, at the
same time, to areas of the social sciences that use text as an object of study or as a source of data. Among other things, the aim is to
provide participants from the humanities and social sciences with specialised research tools for analysing different types of texts (from
newspaper articles to interviews to digital communication records), and to present the standards and traditions of writing scholarly articles
using selected methods of textual analysis. The workshop series consists of two complementary parts. The first focuses on methodologies
emerging from computational social science and natural language processing (NLP), where large collections of texts are processed and
analysed using programming languages such as Python or R. The second part presents qualitative analysis methods using content
analysis software (Atlas.ti or MaxQDA). No knowledge of programming or qualitative content analysis software is required. The workshop
will be led by experienced researchers from a variety of academic disciplines who look at text from different perspectives: linguists,
sociologists, political scientists, literary scholars and computer scientists. Classes will be held once every two months at a fixed time (3 per
semester, 4h). Number of places limited to 25. Enrolment is compulsory, and 10 places will be reserved for doctoral students who have
declared their intention to attend the whole course.
Course coordinators
Learning outcomes
Knowledge | The graduate knows and understands:
WG_03 - research methodology within social science disciplines
Skills | The graduate is able to:
UW_01 – make use of knowledge from various fields of science, in particular the social sciences in order to creatively identify, formulate
and innovatively solve complex problems or perform tasks of a research nature, and in particular to: define the purpose and object of
scientific research in the field of the social sciences, formulate a research hypothesis; develop research methods, techniques and tools
and apply them creatively; make inferences based on scientific findings
UK_04 - participating in scientific discourse in the field of the social sciences
Social competences | The graduate is ready to
KK_01 - critically evaluating achievements within a given scientific discipline in the field of the social sciences
Assessment criteria
Description of requirements related to participation in classes, including the
permitted number of explained absences: 70% attendance required
Principles for passing the classes and the subject (including resit session): A written paper consisting of a methodological note on a text
mining/analysis project; according to selected methods discussed in class
Methods for the verification of learning outcomes: A written paper consisting of a methodological note on a text mining/analysis project;
according to selected methods discussed in class.
Evaluation criteria: Use of at least two methods discussed in the workshop. Adequate choice of methods in view of the objectives of the
study
Practical placement
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Bibliography
Salganik, M. (2017). Bit by Bit: Social Research in the Digital Age. Princeton: Princeton University Press. Friese, Susane, Qualitative data
analysis with ATLAS. ti. Sage, 2019Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. Text as data: A new framework for
machine learning and the social sciences. Princeton University Press, 2022.Hobson Lane, Cole Howard, Hannes Hapke. Przetwarzanie
języka naturalnego w akcji. Rozumienie, analiza i generowanie tekstu w Pythonie na przykładzie języka angielskiego. Wydawnictwo
Naukowe PWN, 2021McLevey, John. Doing computational social science: a practical introduction. Sage, 2021
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: