Overview

The proliferation and prevalence of conspiracy theories (CTs) online have become one of the most prominent concerns in today’s world. Widespread conspiracy theories have brought tremendous detrimental impact to people in terms of increasing political polarization, promoting extremism, spreading anti-scientific knowledge, and misleading the way people make sense of the world.

This project seeks to understand the narrative, diffusion and socio-political effects of CTs that emerged during the COVID-19 pandemic via computational approaches from the following three interweaving threads. First, it will use network analysis and computer-mediated discourse analysis to discover who the conspirators are and how they apply conspiratorial rhetoric in political uses. Second, it seeks to understand how CTs evolve during transmission and what features cause their strong survivability online. As a result, an automatic CT detection pipeline would be proposed. Third, it will identify influential factors affecting CTs’ diffusion, which are also the key indicators to discuss appropriate debunking methods, informing government policymaking and information regulations.

This project will contribute to the current understanding of the theorization of online conspiracy theories and the design of a more efficient automatic CT detection algorithm. More importantly, it will shed light on future CT management policies.