摘要翻译：
网上个人之间的讨论在形成推动投票、购买、捐赠和其他关键的线下行为的意见方面发挥了关键作用。然而，在网上讨论中，通过说服来改变观点的决定因素在很大程度上仍未被探索。我们的研究考察了$\textit{ethos}$--个人的“声誉”--的说服力，使用了一个7年的小组，从一个包含成功说服的明确指标的论证平台上进行了超过100万次辩论。我们通过从过去辩论竞争的度量中构建声誉工具，并通过在双机器学习框架中使用语言神经模型控制非结构化论辩文本，来识别声誉对说服的因果效应。我们发现，一个人的声誉显著地影响着他们的说服率，超出了他们论点的有效性、强度和陈述。在我们的设置中，我们发现有10个额外的声誉点会使成功说服的概率比平台平均水平增加31%。我们还发现，声誉的影响受到论据内容特征的调节，这种方式与理论模型相一致，该模型将声誉的说服力归因于认知超载下的启发式信息处理。我们讨论了为公共和私人组织在线决策提供便利的平台的管理含义。
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英文标题：
《Influence via Ethos: On the Persuasive Power of Reputation in
  Deliberation Online》
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作者：
Emaad Manzoor, George H. Chen, Dokyun Lee, Michael D. Smith
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Computer Science        计算机科学
二级分类：Computation and Language        计算与语言
分类描述：Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
涵盖自然语言处理。大致包括ACM科目I.2.7类的材料。请注意，人工语言（编程语言、逻辑学、形式系统）的工作，如果没有明确地解决广义的自然语言问题（自然语言处理、计算语言学、语音、文本检索等），就不适合这个领域。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  Deliberation among individuals online plays a key role in shaping the opinions that drive votes, purchases, donations and other critical offline behavior. Yet, the determinants of opinion-change via persuasion in deliberation online remain largely unexplored. Our research examines the persuasive power of $\textit{ethos}$ -- an individual's "reputation" -- using a 7-year panel of over a million debates from an argumentation platform containing explicit indicators of successful persuasion. We identify the causal effect of reputation on persuasion by constructing an instrument for reputation from a measure of past debate competition, and by controlling for unstructured argument text using neural models of language in the double machine-learning framework. We find that an individual's reputation significantly impacts their persuasion rate above and beyond the validity, strength and presentation of their arguments. In our setting, we find that having 10 additional reputation points causes a 31% increase in the probability of successful persuasion over the platform average. We also find that the impact of reputation is moderated by characteristics of the argument content, in a manner consistent with a theoretical model that attributes the persuasive power of reputation to heuristic information-processing under cognitive overload. We discuss managerial implications for platforms that facilitate deliberative decision-making for public and private organizations online.
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PDF链接：
https://arxiv.org/pdf/2006.00707