Conspiracy in the Time of Corona: Automatic detection of Covid-19 Conspiracy Theories in Social Media and the News
Shadi Shahsavari, Pavan Holur, Timothy R. Tangherlini, Vwani, Roychowdhury

TL;DR
This paper develops automated machine-learning methods to detect and analyze conspiracy theories related to Covid-19 on social media and news, revealing how narrative frameworks support rumor proliferation.
Contribution
It introduces a novel approach combining narrative theory with machine learning to identify and monitor conspiracy story frameworks in real-time.
Findings
Identifies key narrative structures in Covid-19 conspiracy theories
Shows how disparate knowledge domains are aligned in these stories
Demonstrates potential for real-time monitoring of rumor vulnerability
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…
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Taxonomy
TopicsMisinformation and Its Impacts · Sentiment Analysis and Opinion Mining · Hate Speech and Cyberbullying Detection
