[论文解读] Conspiracy in the Time of Corona: Automatic detection of Covid-19 Conspiracy Theories in Social Media and the News

Shadi Shahsavari, Pavan Holur+2 more|arXiv (Cornell University)|Apr 28, 2020

Misinformation and Its Impacts参考文献 46被引用 30

一句话总结

该论文提出一个图形叙事模型，自动检测并追踪社交媒体（Reddit 和 4Chan）与新闻（GDELT）中的 Covid-19 阴谋论，揭示叙事如何跨领域交织并随时间演变。

ABSTRACT

Rumors and conspiracy theories thrive in environments of low confidence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of any authoritative scientific consensus on the virus, its spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of property, racially fueled attacks against Asian Americans, and demonstrations espousing resistance to public health orders countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frameworks supporting the generation of these stories. We show how the various narrative frameworks fueling rumors and conspiracy theories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread.

研究动机与目标

• 通过 Covid-19 阴谋论在低信任信息环境中的普遍性及现实影响来激发本研究。
• 提出一个自动化流程，揭示生成阴谋故事的潜在叙事框架。
• 分析社交媒体叙事与新闻报道之间的相互作用，以监测近实时动态。
• 识别叙事簇并跟踪理论如何在跨平台中成核、对齐和传播。

提出的方法

• 将叙事建模为具有行动者的图形网络，边用关系标注，情境塑造关系分布。
• 利用依存解析和语义角色标注从句子中提取模式元组 (arg1, rel, arg2)，再通过 BERT 嵌入将名词短语聚类成情境组。
• 构建子节点（情境组）和边，形成带标签、带权重的叙事图，表示框架。
• 使用基于 Louvain 的方法检测具有重叠结构的社区，以揭示叙事网络中的领域特定簇。
• 在滚动的 5 天窗口中汇集社交媒体数据（4Chan、Reddit）和新闻数据（GDELT），以比较和监测信息流。
• 通过使用标准聚类指标，将社交媒体派生的框架与新闻派生的图进行互相引用来评估社区。

实验结果

研究问题

• RQ1 社交媒体中的 Covid-19 阴谋论背后的叙事框架是什么，它们如何与新闻报道连接？
• RQ2 行动者及其关系如何随时间重新对齐，以在跨平台形成、成核或并入阴谋论？
• RQ3 近实时管道是否能够揭示社交媒体与新闻之间阴谋叙事的动态与流动？
• RQ4 社交媒体叙事网络中出现了哪些社区，它们与新闻内容中观察到的模式有何对应？

主要发现

• 该管道在社交媒体语料中识别出 52 个社区，代表多样的知识领域。
• 阴谋论经常将不同领域对齐，形成更大、最具包容性的叙事，包括与反疫苗等主题的连接。
• 新理论出现（例如 5G 作为根本原因），现有理论随时间成核或与其他理论合并。
• 社交媒体社区与新闻报道之间存在可衡量的信息流，且两大语料库之间存在时间相关模式。
• 每日监测捕捉叙事图的涨落，并突出新闻领域易被阴谋论者再次解读的区域。