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Large-scale evaluation framework for local influence theories in Twitter

机译：Twitter中本地影响力理论的大规模评估框架

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摘要

Influence theories constitute formal models that identify those individuals that are able to affect and guide their peers through their activity. There is a large body of work on developing such theories, as they have important applications in viral marketing, recommendations, as well as information retrieval. Influence theories are typically evaluated through a manual process that cannot scale to data voluminous enough to draw safe, representative conclusions. To overcome this issue, we introduce in this paper a formalized framework for large-scale, automatic evaluation of topic-specific influence theories that are specialized in Twitter. Basically, it consists of five conjunctive conditions that are indicative of real influence exertion: the first three determine which influence theories are compatible with our framework, while the other two estimate their relative effectiveness. At the core of these two conditions lies a novel metric that assesses the aggregate sentiment of a group of users and allows for estimating how close the behavior of influencers is to that of the entire community. We put our framework into practice using a large-scale test-bed with real data from 75 Twitter communities. In order to select the theories that can be employed in our analysis, we introduce a generic, two-dimensional taxonomy that elucidates their functionality. With its help, we ended up with five established topic-specific theories that are applicable to our settings. The outcomes of our analysis reveal significant differences in their performance. To explain them, we introduce a novel methodology for delving into the internal dynamics of the groups of influencers they define. We use it to analyze the implications of the selected theories and, based on the resulting evidence, we propose a novel partition of influence theories in three major categories with divergent performance.
机译：影响力理论构成了正式的模型，这些模型确定了能够影响和指导同伴通过其活动进行活动的个人。开发此类理论的工作量很大，因为它们在病毒式营销，推荐以及信息检索中具有重要的应用。影响力理论通常是通过手动过程进行评估的，该过程无法扩展到足以得出安全，有代表性的结论的大量数据。为了克服这个问题，我们在本文中介绍了一个正式的框架，用于大规模，自动评估专门针对Twitter的特定主题的影响力理论。基本上，它由五个表示实际影响力发挥作用的联合条件组成：前三个条件确定哪些影响力理论与我们的框架兼容，而其他两个条件则评估它们的相对有效性。这两个条件的核心是一种新颖的度量标准，该度量标准可以评估一组用户的总体情绪，并可以估计影响者的行为与整个社区的行为有多接近。我们使用大型测试平台将其框架付诸实践，其中包含来自75个Twitter社区的真实数据。为了选择可以在我们的分析中使用的理论，我们介绍了一种通用的二维分类法，以阐明其功能。在它的帮助下，我们最终得出了五种适用于我们的设置的特定于主题的理论。我们的分析结果表明，它们的性能存在显着差异。为了解释它们，我们引入了一种新颖的方法来研究他们定义的影响者群体的内部动力。我们用它来分析所选理论的含义，并根据得到的证据，在三个主要类别中将影响理论提出新颖的划分，表现各不相同。

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