FacetClouds: Exploring Tag Clouds for Multi-Dimensional Data

Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s):
  • Visual Computing: Illustrative Visualization
• Date: May 2013
• ISBN: 978-1-4822-1680-6
• Publisher: ACM Publishing House
• Organization: ACM Siggraph
• Location: Regina, Saskatchewan, Canada
• Lecturer: Johann Schrammel
• Address: Regina, Saskatchewan, Canada
• Booktitle: Proceedings of the 2013 Graphics Interface Conference
• Conference date: 29. May 2013 – 31. May 2013
• Pages: 17 – 24

Abstract

Tag clouds are simple yet very widespread representations of how often certain words appear in a collection. In conventional tag clouds, only a single visual text variable is actively controlled: the tags’ font size. Previous work has demonstrated that font size is indeed the most influential visual text variable. However, there are other variables, such as text color, font style and tag orientation, that could be manipulated to encode additional data dimensions.

FacetClouds manipulate intrinsic visual text variables to encode multiple data dimensions within a single tag cloud. We conducted a series of experiments to detect the most appropriate visual text variables for encoding nominal and ordinal values in a cloud with tags of varying font size. Results show that color is the most expressive variable for both data types, and that a combination of tag rotation and background color range leads to the best overall performance when showing multiple data dimensions in a single tag cloud.

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BibTeX

@inproceedings{waldner-2013-facetCloudsGI,
  title =      "FacetClouds: Exploring Tag Clouds for Multi-Dimensional Data",
  author =     "Manuela Waldner and Johann Schrammel and Michael Klein and
               Katrin Kristjansdottir and Dominik Unger and Manfred
               Tscheligi",
  year =       "2013",
  abstract =   "Tag clouds are simple yet very widespread representations of
               how often certain words appear in a collection. In
               conventional tag clouds, only a single visual text variable
               is actively controlled: the tags’ font size. Previous work
               has demonstrated that font size is indeed the most
               influential visual text variable. However, there are other
               variables, such as text color, font style and tag
               orientation, that could be manipulated to encode additional
               data dimensions.  FacetClouds manipulate intrinsic visual
               text variables to encode multiple data dimensions within a
               single tag cloud. We conducted a series of experiments to
               detect the most appropriate visual text variables for
               encoding nominal and ordinal values in a cloud with tags of
               varying font size. Results show that color is the most
               expressive variable for both data types, and that a
               combination of tag rotation and background color range leads
               to the best overall performance when showing multiple data
               dimensions in a single tag cloud. ",
  month =      may,
  isbn =       "978-1-4822-1680-6 ",
  publisher =  "ACM Publishing House",
  organization = "ACM Siggraph",
  location =   "Regina, Saskatchewan, Canada",
  address =    "Regina, Saskatchewan, Canada",
  booktitle =  "Proceedings of the 2013 Graphics Interface Conference",
  pages =      "17--24",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2013/waldner-2013-facetCloudsGI/",
}