An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics: Position Paper

TL;DR

This paper discusses the importance of rigorous evaluation and experimental design in visual text analytics, emphasizing interdisciplinary approaches to address challenges like data ambiguity and user trust to improve reliability and adoption.

Contribution

It highlights key interdisciplinary challenges in evaluating visual text analytics and proposes research directions to enhance validation and trustworthiness.

Findings

Identifies four major challenges: data ambiguity, experimental design, user trust, and big picture concerns.

Emphasizes interdisciplinary approaches from visualization and NLP communities.

Provides suggestions for future research opportunities.

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 computational 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 opportunities from an interdisciplinary perspective.