Getting our head in the clouds: toward evaluation studies of tagclouds

By A. Rivadeneira, D. Gruen, Michael J. Muller, David R. Millen. International Conference on Human Factors in Computing Systems, 2007

426 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/22384011438b9e35b32f65deef03cc9b37aeffaf

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Citations in the corpus

Interpretation and trust: designing model-driven visualizations for text analysis (2012, background): “Despite documented perceptual issues [39], word clouds are regularly found both in analysis tools and across the web [50].”
Dynamic Wordclouds and Vennclouds for Exploratory Data Analysis (2014, background): “Indeed, (Rivadeneira et al., 2007) indicates that words directly adjacent to the largest word in the wordcloud had slightly worse recall than those not-directly-adjacent – in essence, getting the most important words in the center may be counterproductive.”
Participatory Visualization with Wordle (2009, methodology): “Even though the basic Wordle layout had been deployed internally in IBM since 2006 [ 8 ], we would not have thought to create a lab test to look at whether people felt “creative” after using a visualization—let alone whether it would help anyone get “boyfriend points.” Placing it on the web, and ena”
Context preserving dynamic word cloud visualization (2010, methodology): “Tag clouds have been used in a wide diversity of applications ranging from the analytical to the emotional [20, 21], even though tag clouds might be more difficult to navigate with than simple lists of words [10, 14] The tag cloud visualization can roughly be categorized into two groups: static tag ”
Word Cloud Explorer: Text Analytics Based on Word Clouds (2014, methodology): “Kaser and Lemire [13], for instance, use slicing trees, nested tables, and rectangle packing to optimize the distribution of space in HTML-based word clouds.”