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Improving online deliberation with argument network visualization

机译:通过参数网络可视化改进在线审议

摘要

Social media are increasingly used to support online debate and facilitate citizens’ engagement in policy and decision-making. Nevertheless the online dialogue spaces we see on the Web today typically provide flat listings of comments, or threads that can be viewed by ‘subject’ line. These are fundamentally chronological views which offer no insight into the logical structure of the ideas, such as the coherence or evidential basis of an argument. This hampers both quality of users’ participation and effective assessment of the state of the debate. We report on an exploratory study in which we observed users interaction with a collective intelligence tool for online deliberation and compared network and threaded visualizations of arguments. We contend that animated argument networks enhance online debate reading when data complexity increases, improve understanding of the argumentation data model and promote users engagement by improving users emotional reactions to the online discussion tool.
机译:社交媒体越来越多地用于支持在线辩论,并促进公民参与政策和决策。不过,我们今天在网络上看到的在线对话空间通常会提供扁平的评论列表或可以通过“主题”行查看的话题。这些从根本上说是按时间顺序排列的,无法洞悉思想的逻辑结构,例如论点的连贯性或证据基础。这既影响了用户参与的质量,也影响了对辩论状态的有效评估。我们报告了一项探索性研究,在该研究中,我们观察到用户与用于在线审议的集体智能工具的互动,并比较了网络和线程可视化的参数。我们认为,当数据复杂性增加时,动画辩论网络可以增强在线辩论的阅读能力,提高对辩论数据模型的理解,并通过改善用户对在线讨论工具的情感反应来促进用户参与。

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