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Comment Analyzer: A Tool for Analyzing Comment Sets and Thread Structures of News Articles
Bauhaus Universitat Weimar, Virtual Reality and Visualization Group, Germany.
Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.ORCID iD: 0000-0002-6956-466X
Friedrich Schiller University Jena, Empirical Methods of Communication Science, Germany.
German Federal Agency for Civic Education, Germany.
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2025 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 31, no 10, p. 7324-7336Article in journal (Refereed) Published
Abstract [en]

The lack of visually guided data exploration tools limits the scope of research questions communication scientists are able to study. The Comment Analyzer steps in where traditional statistical tools fail when it comes to researching the commenting behavior of news article readers. The basis of such an analysis are comment-thread corpora in which comments are tagged with various deliberative quality indicators as well as political stance. Our analysis tool provides a visual querying system for the exploration and analysis of such corpora and allows social scientists to gain insights into the distributions and relations between comment attributes, the homogeneity of thread sets, frequent thread structures and changes in comment qualities over the course of a single but in particular of multiple threads at once. We developed the tool in close collaboration with communication scientists in a user-centered approach. The system has proven its utility in thorough reviews with the communication scientists, by corroborating existing findings in the literature but particularly by provoking and answering new research questions. Final reviews with five independent experts confirmed these observations and revealed the potential of the Comment Analyzer for other datasets currently being created and analyzed in the communication sciences.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2025. Vol. 31, no 10, p. 7324-7336
Keywords [en]
Graph Visualization, Information Visualization, Multi-Attribute Aggregation, Set-based Visualization, Text Visualization, Tree Aggregation, Visual Text Analytics, Visual analytics, Multi-attributes, News articles, Text analytics, Visual text analytic
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hj:diva-67419DOI: 10.1109/TVCG.2025.3544733ISI: 001566984900016PubMedID: 40031792Scopus ID: 2-s2.0-85218954006Local ID: ;intsam;1944468OAI: oai:DiVA.org:hj-67419DiVA, id: diva2:1944468
Available from: 2025-03-14 Created: 2025-03-14 Last updated: 2025-10-13Bibliographically approved

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Riehmann, Patrick

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