Abbreviating Text Labels on Demand

Contributors

Mariana Shimabukuro, and Christopher Collins

Abstract

A known problem in information visualization labeling is when the text is too long to fit in the label space. There are some common known techniques used in order to solve this problem like setting a very small font size. On the other hand, sometimes the font size is so small that the text can be difficult to read. Wrapping sentences, dropping letters and text truncation can also be used. However, there is no research on how these techniques affect the legibility and readability of the visualization. In other words, we don’t know whether or not applying these techniques is the best way to tackle this issue. This thesis describes the design and implementation of a crowdsourced study that uses a recommendation system to narrow down abbreviations created by participants allowing us to efficiently collect and test the data in the same session. The study design also aims to investigate the effect of semantic context on the abbreviation that the participants create and the ability to decode them. Finally, based on the study data analysis we present a new technique to automatically make words as short as they need to be to maintain text legibility and readability.

The Abbreviation on Demand API

Based on this project we implemented and made available online an API which allows other programmers to use our abbreviation algorithm in their web applications.

API available at: http://abbreviation.vialab.ca

GitHub project: https://github.com/vialab/Abbreviation-On-Demand-API

Publications

  • M. Shimabukuro and C. Collins, “Abbreviating Text Labels on Demand,” Proc. of IEEE Conf. on Information Visualization (InfoVis), 2017. Poster Paper.
    [Bibtex] [PDF]
    @poster{shi2017a,
    author = {Mariana Shimabukuro and Christopher Collins},
    title = {Abbreviating Text Labels on Demand},
    venue = {Proc. of IEEE Conf. on Information Visualization (InfoVis)},
    series = {Poster},
    address = {Phoenix, USA},
    year = 2017,
    note = {Poster Paper}
    }
  • M. Shimabukuro and C. Collins, “Abbreviating Text Labels on Demand,” Proc. of IEEE Conf. on Information Visualization (InfoVis), 2017. Poster.
    [Bibtex] [PDF]
    @poster{shi2017p,
    author = {Mariana Shimabukuro and Christopher Collins},
    title = {Abbreviating Text Labels on Demand},
    venue = {Proc. of IEEE Conf. on Information Visualization (InfoVis)},
    series = {Poster},
    address = {Phoenix, USA},
    year = 2017,
    note = {Poster}
    }
  • M. Shimabukuro, “An Adaptive Crowdsourced Investigation of Word Abbreviation Techniques for Text Visualizations,” , 2017. Master’s Thesis.
    [Bibtex] [PDF]
    @article{shi2017,
      title={An Adaptive Crowdsourced Investigation of Word Abbreviation Techniques for Text Visualizations},
      author={Mariana Shimabukuro},
      year=2017,
      note={Master’s Thesis}
    }

Demo and Supplemental Materials

For some demos applying our “Abbreviation on Demand” algorithm, and some visualizations of our study data access: http://vialab.science.uoit.ca/abbrVisualization/

Project Video

Gallery

 

Research

EduApps – Supporting Non-Native English Speakers to Overcome Language Transfer Effects

Metatation: Annotation as Implicit Interaction to Bridge Close and Distant Reading

DataTours: A Data Narratives Framework

Perceptual Biases in Font Size as a Data Encoding

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Abbreviating Text Labels on Demand

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

PhysioEx: Visual Analysis of Physiological Event Streams

Using Visual Analytics of Heart Rate Variation to Aid in Diagnostics

Off-Screen Desktop

PivotSlice

Reading Comprehension on Mobile Devices

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

Lexichrome

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization

DimpVis

Glidgets

TandemTable

Simple Multi-Touch Toolkit

Exploring Text Entities with Descriptive Non-photorealistic Rendering

Investigating the Semantic Patterns of Passwords

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations

Parallel Tag Clouds to Explore Faceted Text Corpora

VisLink: Revealing Relationships Amongst Visualizations

DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

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