Digging text viz: An archaeological review of ACM digital library text visualizations publications (1991-2003)
SIGDOC 2017 - 35th ACM International Conference on the Design of Communication, Page: 1-13
2017
- 20Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Text visualization is a rapidly growing area of research and practice in the design of communication [1]. But, as text analysis and particularly big data techniques, become more useful across knowledge domains, text visualization will become less of a specialty area and more of a crucial aspect of everyday work. !is paper offers an integrated review of 42 text visualization projects published by an Association for Computing Machinery organization and hosted in the Association for Computing Machinery Digital Library from 1991-2003. !is survey will trace trends in text visualizations projects that specifically apply to the work of designers, user experience researchers, technical communicators and provide a view of the current state of text visualization to be!er approach evolving frameworks and methods to conduct analysis.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know