[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被引用 33

一句话总结

本论文提出一个管线自动检测并分析Covid-19阴谋论背后的叙事框架，通过从社交媒体（4Chan、Reddit）和新闻（GDELT）提取并连接行动者与关系来实现分析。它研究这些叙事如何在不同平台对齐并随时间演变。

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

• 了解 Covid-19 阴谋论如何通过潜在叙事框架生成。
• 开发一个自动化管线，从社交媒体和新闻来源提取行动者和关系。
• 量化疫情期间社交媒体与新闻之间关于阴谋论的跨域信息流。

提出的方法

• 将叙事建模为图形网络，其中行动者为节点，带标签的边表示关系。
• 使用依存句法分析和语义角色标注从句子中提取关系元组，并将名词短语聚类为上下文组（CGs）。
• 使用基于 BERT 的嵌入将短语聚类为子节点，形成叙事图中的微观上下文。
• 将社交媒体帖子聚合为五日段，并为每个段构建共现行动者网络。
• 使用 TF-IDF 过滤和共现网络将社交媒体派生的叙事与新闻报道进行比较，以研究跨语料动态。
• 随时间评估社区，使用覆盖度、同质性、完整性和 V-measure 指标。

实验结果

研究问题

• RQ1 哪些叙事框架构成社交媒体与新闻中的 Covid-19 阴谋论？
• RQ2 行动者及其关系如何在社交媒体与新闻之间随时间对齐？
• RQ3 自动化管线能否揭示阴谋叙事从社交平台到新闻媒体的流动？

主要发现

• 该管线在阴谋叙事中识别出五个核心现象，包括与现有理论的整合、与5G相关理论的出现，以及与反疫苗叙事的一致性。
• 一个每日更新的叙事图揭示故事互联的涨落，并突出易被阴谋论重新解读的新闻片段。
• 社交媒体社群在新闻报道中具有可检测的存在，便于跨语料监测信息流。
• 该方法产生了 52 个由社交媒体派生的社区，值得注意的行动者包括 China、Trump、virus、Gates 等，推动叙事结构。
• 研究表明尽管社交媒体数据噪声较大，叙事框架仍可随时间进行追踪与比较。