Modeling Controversy within Populations
Modeling Controversy within Populations
复制标题
群体内部的建模争议
DOI：
10.1145/3121050.3121067
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J. Allan
中科院分区：
文献类型：
--
作者：
Myungha Jang;Shiri Dori;J. Allan
越来越多的研究侧重于通过计算检测有争议的话题并了解人们对这些话题的立场。然而，我们对如何定义争议、争议如何表现以及如何衡量争议的理论和实践理解仍然存在差距。由于争议是一种复杂的社会现象，很难理解争议是由哪些因素构成的。之前的工作试图通过研究不同立场群体之间分歧和极性的线索来从算法上捕捉争议。然而，我们对于如何定义和衡量争议仍然缺乏系统的理解。在本文中，我们提出了一个多维争议模型。具体来说，我们引入了一个具有两个最小维度的模型：竞争和重要性。我们的模型与现有的工作不同，它将争议视为植根于人口的特征。它表明，争议应该在特定人群中单独观察，而不是作为一个固定的普遍数量。我们从数学的角度对群体内的竞争和重要性进行建模。为了验证和评估我们理论模型的可靠性，我们将模型实例化为多种来源的算法：民意调查、Twitter 和维基百科。我们证明，我们的争议模型不仅对观察到的现象具有解释力，而且对任务也具有预测力，例如识别有争议的维基百科文章。
A growing body of research focuses on computationally detecting controversial topics and understanding the stances people hold on them. Yet gaps remain in our theoretical and practical understanding of how to define controversy, how it manifests, and how to measure it. Since controversy is a complicated social phenomenon, it is difficult to understand what elements make up the controversy. Previous work has attempted to capture controversy algorithmically by studying cues for disagreement and polarity between different stance groups. However, we still lack a systematic understanding of how controversy should be defined and measured. In this paper, we propose a multi-dimensional model of controversy. Specifically, we introduce a model with two minimal dimensions: contention and importance. Our model departs from existing work by viewing controversy as a trait rooted in population. It suggests that controversy should be separately observed in a given population, rather than held as a fixed universal quantity. We model contention and importance within a population from a mathematical standpoint. To validate and evaluate the soundness of our theoretical model, we instantiate the model to algorithms for a diverse set of sources: polling, Twitter, and Wikipedia. We demonstrate that our controversy model holds an explanatory power for observed phenomena but also a predictive power for tasks, such as identifying controversial Wikipedia articles.