Persuading people to change their opinions is a common practice in online
discussion forums on topics ranging from political campaigns to relationship
consultation. Enhancing people's ability to write persuasive arguments could
not only practice their critical thinking and reasoning but also contribute to
the effectiveness and civility in online communication. It is, however, not an
easy task in online discussion settings where written words are the primary
communication channel. In this paper, we derived four design goals for a tool
that helps users improve the persuasiveness of arguments in online discussions
through a survey with 123 online forum users and interviews with five debating
experts. To satisfy these design goals, we analyzed and built a labeled dataset
of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and
evidence) in 164 arguments with high ratings on persuasiveness from
ChangeMyView, a popular online discussion forum. We then designed an
interactive visual system, Persua, which provides example-based guidance on
persuasive strategies to enhance the persuasiveness of arguments. In
particular, the system constructs portfolios of arguments based on different
persuasive strategies applied to a given discussion topic. It then presents
concrete examples based on the difference between the portfolios of user input
and high-quality arguments in the dataset. A between-subjects study shows
suggestive evidence that Persua encourages users to submit more times for
feedback and helps users improve more on the persuasiveness of their arguments
than a baseline system. Finally, a set of design considerations was summarized
to guide future intelligent systems that improve the persuasiveness in text.

这篇论文通过调查问卷和专家访谈，研究讨论区中使用的四种设计目标，进而构建了一个标记数据集以分析和提高辩论的说服力。使用基于不同说服策略的包含具体案例的分析工具，Persua 鼓励用户提交更多反馈，帮助他们在辩论中提高说服力，同时该系统设计考虑也有启示意义。

Persua：一种用于增强在线讨论中论点说服力的可视化交互系统

Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion

The need for interpretable and accountable intelligent systems grows along
with the prevalence of artificial intelligence applications used in everyday
life. Explainable intelligent systems are designed to self-explain the
reasoning behind system decisions and predictions, and researchers from
different disciplines work together to define, design, and evaluate
interpretable systems. However, scholars from different disciplines focus on
different objectives and fairly independent topics of interpretable machine
learning research, which poses challenges for identifying appropriate design
and evaluation methodology and consolidating knowledge across efforts. To this
end, this paper presents a survey and framework intended to share knowledge and
experiences of XAI design and evaluation methods across multiple disciplines.
Aiming to support diverse design goals and evaluation methods in XAI research,
after a thorough review of XAI related papers in the fields of machine
learning, visualization, and human-computer interaction, we present a
categorization of interpretable machine learning design goals and evaluation
methods to show a mapping between design goals for different XAI user groups
and their evaluation methods. From our findings, we develop a framework with
step-by-step design guidelines paired with evaluation methods to close the
iterative design and evaluation cycles in multidisciplinary XAI teams. Further,
we provide summarized ready-to-use tables of evaluation methods and
recommendations for different goals in XAI research.

本文旨在分享跨学科 XP 机器学习设计和评估方法的知识和经验，并提出了 XP 智能系统设计目标和评估方法的分类以支持 XP 研究的多样性设计目标和评估方法。我们为不同 XP 用户组的设计目标和其评估方法之间的映射提供了一个分类，以及一系列的设计指南和与之相对应的评估方法，从而关闭多学科 XP 团队的迭代设计和评估循环。