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Using Argumentative Structure to Interpret Debates in Online Deliberative Democracy and eRulemaking

机译：利用争论结构来解释在线审议民主和骚扰中的争论

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摘要

Governments around the world are increasingly utilising online platforms and social media to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. In this article, we show how the analysis of argumentative and dialogical structures allows for the principled identification of those issues that are central, controversial, or popular in an online corpus of debates. Although areas such as controversy mining work towards identifying issues that are a source of disagreement, by looking at the deeper argumentative structure, we show that a much richer understanding can be obtained. We provide results from using a pipeline of argument-mining techniques on the debate corpus, showing that the accuracy obtained is sufficient to automatically identify those issues that are key to the discussion, attracting proportionately more support than others, and those that are divisive, attracting proportionately more conflicting viewpoints.
机译：世界各国政府越来越多地利用在线平台和社交媒体与其公民的意见。虽然政策制定者可能会从社会的这种巨大反馈中受益，但他们首先面临挑战的挑战，从而从产生的大量数据产生。在本文中，我们展示了争论性和对话结构的分析如何识别这些问题，这些问题是中央，争议或在辩论的在线语料库中。尽管通过观察更深层次的辩论结构，但争议挖掘争议的领域是识别分歧来源的问题，但我们表明可以获得更丰富的理解。我们提供了在辩论语料库上使用论证挖掘技术的管道，表明所获得的准确性足以自动识别讨论的关键，吸引比其他问题的问题，以及分裂，吸引的那些问题比例更加冲突的观点。

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