IQLA-GIAT Summer School in Quantitative Analysis of Textual DataCamps 27.05.2019
Recent developments in digital methods are not only changing how research is conducted in the humanities and social sciences, but also how new research is planned and designed. In order to reach the full potential and benefits of this revolution, most research activities need a new generation of researchers: data scientists for humanities and social sciences.
Digital methods are being utilised by a variety of disciplines. The growing availability of large corpora and large databases calls for new methods that are able to deal with new problems, open the door to new questions and develop new knowledge.
“Distant Reading”, “Digital Methods”, “Computational social sciences” and “Statistical learning from textual data” are general terms that refer to a wide range of methods that have a common aim: retrieving information from texts by means of computer-aided tools. Today, computer-aided text analysis is an umbrella term referring to a number of qualitative, quantitative and mixed-methods approaches. It is an object of research in many sectors of linguistics, computer sciences, mathematics and statistics. Furthermore, computer aided text analysis is used as a research tool within a number of disciplines such as psychology, philosophy, sociology, sociolinguistics, education, history, political studies, literary studies, communication and media studies. The recent evolution of information technologies (IT) and computational methods has led to a number of distinct but interrelated sectors (e.g. computational linguistics, information retrieval, natural language processing, text mining, text analytics, sentiment analysis, opinion mining, topic extraction, etc.) with interesting industrial applications, such as electronic dictionaries, artificial intelligence, plagiarism detection and similar.
Recent studies have stressed the need for developing, adopting and sharing interdisciplinary approaches. The IQLA-GIAT Summer School is the ideal environment for developing innovative analytical tools by pooling together the research methods from different disciplines.
The IQLA-GIAT Summer School is characterized by three main elements:
- a general part devoted to quantitative methods;
- a special issue that has changed over time (2019: Data Science and Data scientists in Humanities and Social Sciences);
- several lab-sessions dedicated to computer-aided analysis of textual data.
Teaching activities at the School will raise questions that can be answered thanks to quantitative methods implemented within a text analysis framework and other procedures that may be used to identify and compare text characteristics. The aim is to discuss the strengths, weaknesses, opportunities and threats of quantitative methods for text analysis with postgraduate students, early career researchers and scholars of different disciplines. The Summer School aims at:
- sharing information on software, corpora, relevant literature and research results;
- promoting a dialogue among different disciplines on emerging research issues;
- developing innovative analytical tools and integrated research methods;
- introducing postgraduate students and early career researchers to new strains of research and applications;
- sharing state-of-the-art techniques in digital methods for text analysis (topic detection, text classification, data visualization).
The IQLA-GIAT Summer School is open to 20 participants including researchers, scholars and postgraduate students. The selection of 20 participants is due to the capacity of the laboratory room.
Tuition fee 250 €
Applicants should send a file in pdf format including:
- curriculum vitae;
- personal mission statement and research interests (max 500 words);
Applications should be sent to the following address: firstname.lastname@example.org
Deadline June, 27th
For any further information and details about terms, deadlines, application forms and payment methods, please contact:
- email@example.com (Prof.ssa Arjuna Tuzzi)
- firstname.lastname@example.org (Dott. Stefano Sbalchiero)