Communication between people is characterized by a broad range of nonverbal
cues. Transferring these cues into the design of robots and other artificial
agents that interact with people may foster more natural, inviting, and
accessible experiences. In this position paper, we offer a series of definitive
nonverbal codes for human-robot interaction (HRI) that address the five human
sensory systems (visual, auditory, haptic, olfactory, gustatory) drawn from the
field of communication studies. We discuss how these codes can be translated
into design patterns for HRI using a curated sample of the communication
studies and HRI literatures. As nonverbal codes are an essential mode in human
communication, we argue that integrating robotic nonverbal codes in HRI will
afford robots a feeling of "aliveness" or "social agency" that would otherwise
be missing. We end with suggestions for research directions to stimulate work
on nonverbal communication within the field of HRI and improve communication
between human and robots.

本文提出将非语言暗示转化为人机交互设计的解决方案，通过遵循沟通学的五个人类感官系统（视觉，听觉，触觉，嗅觉，味觉）的非语言暗示准则来实现。并论述了将这些准则应用到人机交互设计模式的设计中，并给出了研究方向，以推进人机交互领域的非语言沟通和改进人机沟通的方向。

人机交互中的非语言提示：传播学视角

Nonverbal Cues in Human-Robot Interaction: A Communication Studies  Perspective

Scientific topics, claims and resources are increasingly debated as part of
online discourse, where prominent examples include discourse related to
COVID-19 or climate change. This has led to both significant societal impact
and increased interest in scientific online discourse from various disciplines.
For instance, communication studies aim at a deeper understanding of biases,
quality or spreading pattern of scientific information whereas computational
methods have been proposed to extract, classify or verify scientific claims
using NLP and IR techniques. However, research across disciplines currently
suffers from both a lack of robust definitions of the various forms of
science-relatedness as well as appropriate ground truth data for distinguishing
them. In this work, we contribute (a) an annotation framework and corresponding
definitions for different forms of scientific relatedness of online discourse
in Tweets, (b) an expert-annotated dataset of 1261 tweets obtained through our
labeling framework reaching an average Fleiss Kappa $\kappa$ of 0.63, (c) a
multi-label classifier trained on our data able to detect science-relatedness
with 89% F1 and also able to detect distinct forms of scientific knowledge
(claims, references). With this work we aim to lay the foundation for
developing and evaluating robust methods for analysing science as part of
large-scale online discourse.

本研究使用一个标注框架和对不同形式科学相关性的定义，对推文中的科学相关信息进行标注，通过训练多标签分类器检测科学相关性，并成功检测出不同范畴的科学知识（声明、参考文献）进行分析。