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Rumors and conspiracy theories thrive in environments of low confidence andlow trust. Consequently, it is not surprising that ones related to the Covid-19pandemic are proliferating given the lack of any authoritative scientificconsensus on the virus, its spread and containment, or on the long term socialand economic ramifications of the pandemic. Among the stories currentlycirculating are ones suggesting that the 5G network activates the virus, thatthe pandemic is a hoax perpetrated by a global cabal, that the virus is abio-weapon released deliberately by the Chinese, or that Bill Gates is using itas cover to launch a global surveillance regime. While some may be quick todismiss these stories as having little impact on real-world behavior, recentevents including the destruction of property, racially fueled attacks againstAsian Americans, and demonstrations espousing resistance to public healthorders countermand such conclusions. Inspired by narrative theory, we crawlsocial media sites and news reports and, through the application of automatedmachine-learning methods, discover the underlying narrative frameworkssupporting the generation of these stories. We show how the various narrativeframeworks fueling rumors and conspiracy theories rely on the alignment ofotherwise disparate domains of knowledge, and consider how they attach to thebroader reporting on the pandemic. These alignments and attachments, which canbe monitored in near real-time, may be useful for identifying areas in the newsthat are particularly vulnerable to reinterpretation by conspiracy theorists.Understanding the dynamics of storytelling on social media and the narrativeframeworks that provide the generative basis for these stories may also behelpful for devising methods to disrupt their spread.

Conspiracy in the Time of Corona: Automatic detection of Covid-19 Conspiracy Theories in Social Media and the News