An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics : Position Paper
(2022)- Abstract
- Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of... (More)
- Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond com- putational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and “big picture” concerns) and provide suggestions for research oppor- tunities from an interdisciplinary perspective. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d4c58beb-1d71-48c3-a0c1-7ccb54f147e8
- author
- Kucher, Kostiantyn ; Sultanum, Nicole ; Daza, Angel ; Simaki, Vasiliki LU ; Skeppstedt, Maria ; Plank, Barbara ; Fekete, Jean-Daniel and Mahyar, Narges
- organization
- publishing date
- 2022
- type
- Working paper/Preprint
- publication status
- published
- subject
- keywords
- Human-centered computing, Visualization, Visualization design and evaluation methods
- pages
- 10 pages
- publisher
- arXiv.org
- DOI
- 10.48550/arXiv.2209.11534
- language
- English
- LU publication?
- yes
- additional info
- To appear in Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization (BELIV '22)
- id
- d4c58beb-1d71-48c3-a0c1-7ccb54f147e8
- date added to LUP
- 2022-11-29 08:35:57
- date last changed
- 2023-03-21 15:34:57
@misc{d4c58beb-1d71-48c3-a0c1-7ccb54f147e8,
abstract = {{Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond com- putational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and “big picture” concerns) and provide suggestions for research oppor- tunities from an interdisciplinary perspective.}},
author = {{Kucher, Kostiantyn and Sultanum, Nicole and Daza, Angel and Simaki, Vasiliki and Skeppstedt, Maria and Plank, Barbara and Fekete, Jean-Daniel and Mahyar, Narges}},
keywords = {{Human-centered computing; Visualization; Visualization design and evaluation methods}},
language = {{eng}},
note = {{Preprint}},
publisher = {{arXiv.org}},
title = {{An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics : Position Paper}},
url = {{http://dx.doi.org/10.48550/arXiv.2209.11534}},
doi = {{10.48550/arXiv.2209.11534}},
year = {{2022}},
}