Visual Text Analytics (Dagstuhl Seminar 22191)

Authors Christopher Collins, Antske Fokkens, Andreas Kerren, Chris Weaver, Angelos Chatzimparmpas and all authors of the abstracts in this report

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Document Identifiers

Author Details

Christopher Collins
  • Ontario Tech University - Oshawa, C
Antske Fokkens
  • Free University - Amsterdam, NL
Andreas Kerren
  • Linköping University - Norrköping, SE
Chris Weaver
  • University of Oklahoma - Norman, US
Angelos Chatzimparmpas
  • Linnaeus University - Växjö, SE
and all authors of the abstracts in this report

Cite AsGet BibTex

Christopher Collins, Antske Fokkens, Andreas Kerren, Chris Weaver, and Angelos Chatzimparmpas. Visual Text Analytics (Dagstuhl Seminar 22191). In Dagstuhl Reports, Volume 12, Issue 5, pp. 37-91, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


Text data is one of the most abundant types of data available, produced every day across all domains of society. Understanding the contents of this data can support important policy decisions, help us understand society and culture, and improve business processes. While machine learning techniques are growing in their power for analyzing text data, there is still a clear role for human analysis and decision-making. This seminar explored the use of visual analytics applied to text data as a means to bridge the complementary strengths of people and computers. The field of visual text analytics applies visualization and interaction approaches which are tightly coupled to natural language processing systems to create analysis processes and systems for examining text and multimedia data. During the seminar, interdisciplinary working groups of experts from visualization, natural language processing, and machine learning examined seven topic areas to reflect on the state of the field, identify gaps in knowledge, and create an agenda for future cross-disciplinary research. This report documents the program and the outcomes of Dagstuhl Seminar 22191 "Visual Text Analytics".

Subject Classification

ACM Subject Classification
  • Human-centered computing → Visualization techniques
  • Human-centered computing → Visual analytics
  • Human-centered computing → Information visualization
  • Computing methodologies → Natural language processing
  • Computing methodologies → Machine learning
  • Information systems → Information systems applications
  • Applied computing → Document management and text processing
  • Information visualization
  • visual text analytics
  • visual analytics
  • text visualization
  • explainable ML for text analytics
  • language models
  • text mining
  • natural language processing


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