Although a great deal of attention has been paid to how conspiracy theories circulate on social media and their factual counterpart conspiracies, there has been little computational work done on describing their narrative structures. We present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories on social media, and actual conspiracies reported in the news media. We base this work on two separate repositories of posts and news articles describing the well-known conspiracy theory Pizzagate from 2016, and the New Jersey conspiracy Bridgegate from 2013. We formulate a graphical generative machine learning model where nodes represent actors/actants, and multi-edges and self-loops among nodes capture context-specific relationships. Posts and news items are viewed as samples of subgraphs of the hidden narrative network. The problem of reconstructing the underlying structure is posed as a latent model estimation problem. We automatically extract and aggregate the actants and their relationships from the posts and articles. We capture context specific actants and interactant relationships by developing a system of supernodes and subnodes. We use these to construct a network, which constitutes the underlying narrative framework. We show how the Pizzagate framework relies on the conspiracy theorists' interpretation of "hidden knowledge" to link otherwise unlinked domains of human interaction, and hypothesize that this multi-domain focus is an important feature of conspiracy theories. While Pizzagate relies on the alignment of multiple domains, Bridgegate remains firmly rooted in the single domain of New Jersey politics. We hypothesize that the narrative framework of a conspiracy theory might stabilize quickly in contrast to the narrative framework of an actual one, which may develop more slowly as revelations come to light.

中文翻译：

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用于发现阴谋和阴谋理论叙事框架的自动化管道：Bridgegate，Pizzagate和网络上的讲故事

尽管已经对阴谋理论如何在社交媒体上传播以及它们的事实阴谋进行了大量关注，但是在描述其叙事结构方面却很少进行计算。我们提供了一个自动管道，用于发现和描述社交媒体上阴谋理论的生成叙事框架以及新闻媒体中报道的实际阴谋。我们基于两个独立的帖子和新闻存储库来开展此项工作，这些存储库分别描述了2016年以来的著名阴谋论Pizzagate和2013年新泽西州的阴谋Bridgegate。我们建立了图形化的机器学习模型，其中节点代表参与者/参与者，并且多节点之间的边缘和自环捕获特定于上下文的关系。帖子和新闻被视为隐藏叙事网络的子图样本。重构底层结构的问题被提出为潜在模型估计问题。我们会自动从帖子和文章中提取并汇总参与者及其关系。我们通过开发超节点和子节点系统来捕获特定于上下文的参与者和交互关系。我们使用它们来构建一个网络，该网络构成了底层的叙事框架。我们将展示Pizzagate框架如何依赖于阴谋理论家对“隐藏知识”的解释，以将人类互动的其他不相关领域联系起来，并假设这种多领域关注是阴谋理论的重要特征。Pizzagate依赖于多个域的对齐方式，布里奇盖特仍然坚定地植根于新泽西政治的单一领域。我们假设，阴谋理论的叙事框架与实际的叙事框架相反，可能会迅速稳定下来，而随着揭露的事实，叙事框架的发展可能会更加缓慢。