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Revealing and Predicting Online Persuasion Strategy with Elementary Units

机译:揭示和预测基本单元的在线说服策略

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In online arguments, identifying how users construct their arguments to persuade others is important in order to understand a persuasive strategy directly. However, existing research lacks empirical investigations on highly semantic aspects of elementary units (EUs). such as propositions for a persuasive online argument. Therefore, this paper focuses on a pilot study, revealing a persuasion strategy using EUs. Our contributions are as follows: (1) annotating five types of EUs in a persuasive forum, the so-called ChatigeMyView. (2) revealing both intuitive and non-intuitive strategic insights for the persuasion by analyzing 4612 annotated EUs, and (3) proposing baseline neural models that identify the EU boundary and type. Our observations imply that EUs definitively characterize online persuasion strategies. The annotated dataset, annotation guideline, and implementation of the neural model are available in public.~1
机译:在在线论证中,识别用户如何构造自己的论据以说服他人很重要,这样才能直接理解说服力策略。但是,现有研究缺乏对基本单元(EU)高度语义化方面的经验研究。例如说服力的在线论点的命题。因此,本文侧重于一项试点研究,揭示使用欧盟的说服策略。我们的贡献如下:(1)在有说服力的论坛中,对五类欧盟进行注释,即所谓的ChatigeMyView。 (2)通过分析4612个带注释的EU来揭示该说服力的直观和非直觉的战略见解,以及(3)提出识别EU边界和类型的基线神经模型。我们的观察结果表明,欧盟最终确定了在线说服策略的特征。带注释的数据集,注释准则和神经模型的实现已公开可用。〜1

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