协同决策支持系统中情绪传染的表征

Amab Sircar, M. Klein
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摘要

本研究的重点是分析人们在网络平台上通过小组讨论参与决策的情感方面是如何表现出来的。特别强调的是对各种问题的讨论的毒性程度。选择了三个平台进行比较研究:第一个是类似reddit的在线论坛,第二个是Polis,参与者可以在这里对问题进行投票,并被吹捧为计算民主的论坛,第三个被称为审议场,由麻省理工学院集体智慧中心开发。这些平台有自己的特定结构,可以帮助小组讨论各种问题。本文研究的一个重要问题是如何表征三种平台的毒性传染以及它们的结构抑制它的程度。我们的方法是首先从三个平台中提取两组实际数据:对照组和实验组,在后者中,通过插入移情声明来抑制毒性使用干预。毒性水平(使用Google Perspective API确定)使用来自三个平台的样本数据进行比较。我们使用生成模型,通过添加来自三个平台的合成节点(小组讨论中的人工条目)来扩展样本数据集,这些节点基于参与者之间的人气、新颖性、根偏差和互惠的参数值。在这些合成节点上添加节点类型和毒性评分两个附加参数。生成模型有助于扩展现有数据集,以确定平台中的毒性传染。这是通过开发毒性传染指数来实现的,ECtox表示为节点总数的百分比。开发这一指数的想法改编自生态学研究中的工作,在生态学研究中,在几种不同类型的斑块存在的情况下,检查某种类型的土地斑块的传染情况。我们使用这个指标来评估三个平台的对照组和实验组的毒性传染。在共情陈述的干预下,审议所的ECtox从1.309%提高到1.297%。然而,在论坛中,ECtox实际上从1.711%上升到1.951%。最后,在波利斯,ECtox从1.750%提高到1.663%。因此,对于审议,毒性的流动是最少的。我们推断,在审议的设计中存在固有的结构结构,这可能会自然地抑制小组讨论中的毒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterization of Emotional Contagion in Collaborative Decision Support Systems
This research is focused on the analysis of how emotional aspects of people participating in decision-making through group discussions in online platforms manifest themselves. A particular emphasis is on the toxicity levels of discussions on various issues. Three platforms have been chosen for a comparative study: the first is a Reddit-like online forum, the second is Polis, where participants may vote on issues and is touted as a forum for computational democracy, and the third is called the Deliberatorium, developed at the Center for Collective Intelligence at the Massachusetts Institute of Technology. These platforms have their own specific structures that aid in group discussions on various issues. An important question that has been examined in this paper is how to characterize the contagion of toxicity in the three platforms and the extent to which their structures inhibit it. Our approach has been to first extract two sets of actual data from the three platforms: a control group and an experimental group where in the latter, an intervention is used by inserting an empathy statement to inhibit toxicity. Toxicity levels (determined using Google Perspective API) were compared using sample data from the three platforms. We use generative models that extend the sample datasets by adding synthetic nodes (artificial entries in group discussions) from the three platforms based on parameter values of popularity, novelty, root-bias, and reciprocity among participants. Two additional parameters of node type and toxicity score were added to these synthetic nodes. The generative models help in extending existing datasets to determine the contagion of toxicity in the platform. This is achieved by developing an index for toxicity contagion, ECtox expressed as a percentage of the total number of nodes. The idea of developing this index has been adapted from work in ecological studies where contagion of a certain type of land patch is examined in the presence of several different patch types. We use this metric to assess toxicity contagion in both the control and experimental groups of the three platforms. With the intervention of the empathy statement, Deliberatorium had an improvement in ECtox from 1.309% to 1.297%. In Forum, however, ECtox actually increased from 1.711% to 1.951%. Finally, in Polis, there was an improvement in ECtox from 1.750% to 1.663%. Thus, for Deliberatorium, the flow of toxicity is the least. We infer that there are inherent structural constructs in the design of the Deliberatorium, and that may naturally inhibit toxicity in group discussions.
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