Explainability of Text Clustering Visualizations-Twitter Disinformation Case Study
While text clustering methods have been available for decades, there is a paucity of material that would help practitioners with the choice and configuration of suitable algorithms and visualizations. In this article, we present a case study analyzing two disinformation datasets composed of tweets f...
| Veröffentlicht in: | IEEE computer graphics and applications. - 1991. - 42(2022), 4 vom: 15. Juli, Seite 8-19 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE computer graphics and applications |
| Schlagworte: | Journal Article |
| Zusammenfassung: | While text clustering methods have been available for decades, there is a paucity of material that would help practitioners with the choice and configuration of suitable algorithms and visualizations. In this article, we present a case study analyzing two disinformation datasets composed of tweets from the era of the 2016 United States Presidential Election. We use this to demonstrate steps for selecting the best configuration of the clustering algorithm and consequently conduct a user experiment for evaluating the comprehensibility of three alternate visualizations. A supplementary GitHub repository contains source code with examples |
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| Beschreibung: | Date Completed 19.07.2022 Date Revised 14.09.2022 published: Print Citation Status MEDLINE |
| ISSN: | 1558-1756 |
| DOI: | 10.1109/MCG.2022.3179914 |