Exploring Patient Organization Periodicals with the Topic Timelines Text Visualization Method

dc.contributor.authorSkeppstedt, Maria
dc.contributor.authorMaen, Adam
dc.contributor.authorDanilova, Vera
dc.contributor.authorAangenendt, Gijs
dc.contributor.authorBurchell, Andrew
dc.contributor.authorSöderfeldt, Ylva
dc.contributor.editorNermo, Magnus
dc.contributor.editorPapadopoulou Skarp, Frantzeska
dc.contributor.editorTienken, Susanne
dc.contributor.editorWidholm, Andreas
dc.contributor.editorBlåder, Anna
dc.date.accessioned2025-12-19T12:50:03Z
dc.date.available2025-12-19T12:50:03Z
dc.date.issued2025
dc.description.abstractThe text visualization technique Topic Timelines offers a compact visualization to represent the evolution and clustering of topics over time, while also providing direct access to the texts in which these topics appear. In this paper, we describe how Topic Timelines was further developed within the ActDisease project, by adding functionality for generating timelines using different types of topic extraction techniques and connecting the visualization to existing interfaces for the close reading of texts. Additionally, we evaluate how the updated temporal topic overview can support corpus exploration. The experiments were conducted on a digitalized corpus from the ActDisease project, consisting of patient organization periodicals from the Swedish Diabetes Association, published between 1949 and 1990. Timelines were generated based on topics extracted using sentence transformers clustering and integrated with the ActDisease text database interface - a user interface developed for exploring and reading texts digitalized within the project.en
dc.identifier.issn1736-6305
dc.identifier.urihttps://hdl.handle.net/10062/118302
dc.language.isoen
dc.publisherTartu University Library
dc.relation.ispartofseriesNEALT Proceedings Series 60
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectText visualization
dc.subjectAutomatic topic extraction
dc.subjectSentence transformers
dc.subjectPatient organizations
dc.titleExploring Patient Organization Periodicals with the Topic Timelines Text Visualization Method
dc.typeArticle

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