Facilitative moderation for online participation in eRulemaking

Joonsuk Park, Sally Klingel, Claire Cardie, Mary J. Newhart, Cynthia Farina, Joan-Josep Vallbé
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引用次数: 21

Abstract

This paper describes the use of facilitative moderation strategies in an online rulemaking public participation system. Rulemaking is one of the U. S. government's most important policymaking methods. Although broad transparency and participation rights are part of its legal structure, significant barriers prevent effective engagement by many groups of interested citizens. Regulation Room, an experimental open-government partnership between academic researchers and government agencies, is a socio-technical participation system that uses multiple methods to lower potential barriers to broader participation. To encourage effective individual comments and productive group discussion in Regulation Room, we adapt strategies for facilitative human moderation originating from social science research in deliberative democracy and alternative dispute resolution [24, 1, 18, 14] for use in the demanding online participation setting of eRulemaking. We develop a moderation protocol, deploy it in "live" Department of Transportation (DOT) rulemakings, and provide an initial analysis of its use through a manual coding of all moderator interventions with respect to the protocol. We then investigate the feasibility of automating the moderation protocol: we employ annotated data from the coding project to train machine learning-based classifiers to identify places in the online discussion where human moderator intervention is required. Though the trained classifiers only marginally outperform the baseline, the improvement is statistically significant in spite of limited data and a very basic feature set, which is a promising result.
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促进在线参与规则制定
本文描述了在一个在线规则制定公众参与系统中使用促进性审核策略。规则制定是美国政府最重要的政策制定方式之一。虽然广泛的透明度和参与权是其法律结构的一部分,但重大障碍阻碍了许多感兴趣的公民群体的有效参与。Regulation Room是学术研究人员和政府机构之间的实验性开放政府合作伙伴关系,是一个社会技术参与系统,它使用多种方法来降低广泛参与的潜在障碍。为了在规则室中鼓励有效的个人评论和富有成效的小组讨论,我们调整了源自协商民主和替代性争议解决的社会科学研究[24,11,18,14]的促进人类适度的策略,用于要求苛刻的在线参与规则制定环境。我们开发了一个调节协议,将其部署到“实时”交通部(DOT)规则制定中,并通过对协议的所有调节干预进行手动编码,对其使用情况进行初步分析。然后,我们研究了自动化审核协议的可行性:我们使用来自编码项目的注释数据来训练基于机器学习的分类器,以识别在线讨论中需要人工版主干预的地方。虽然训练的分类器只略微优于基线,但在有限的数据和非常基本的特征集的情况下,这种改进在统计上是显著的,这是一个很有希望的结果。
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