Town hall-type debates are increasingly moving online, irrevocably transforming public discourse. Yet, we know relatively little about crucial social dynamics that determine which arguments are more likely to be successful. This study investigates the impact of one's position in the discussion network created via responses to others' arguments on one's persuasiveness in unfacilitated online debates. We propose a novel framework for measuring the impact of network position on persuasiveness, using a combination of social network analysis and machine learning. Complementing existing studies investigating the effect of linguistic aspects on persuasiveness, we show that the user's position in a discussion network influences their persuasiveness online. Moreover, the recognition of successful persuasion further increases this dominant network position. Our findings offer important insights into the complex social dynamics of online discourse and provide practical insights for organizations and individuals seeking to understand the interplay between influential positions in a discussion network and persuasive strategies in digital spaces.

翻译：市政厅式辩论正日益转向线上，不可逆转地改变了公共话语形态。然而，我们对决定哪些论点更易成功的核心社会动力学知之甚少。本研究探讨了在无主持人参与的在线辩论中，个体在通过回应他人论点所形成的讨论网络中的位置对其说服力的影响。我们提出了一种新颖框架，通过结合社会网络分析与机器学习来测量网络位置对说服力的影响。作为对现有研究（聚焦语言因素对说服力的影响）的补充，我们证明用户在讨论网络中的位置确实会影响其在线说服力。此外，成功说服的识别会进一步强化这种优势网络地位。我们的发现为理解在线话语中复杂的社会动力学提供了重要洞见，并为寻求把握讨论网络中影响力位置与数字空间说服策略之间相互作用的组织与个人提供了实践启示。