Student Scholar Symposium Abstracts and Posters

Document Type

Poster

Publication Date

Fall 11-30-2022

Faculty Advisor(s)

Dr. Ann Gordon

Abstract

All prior categorizations of conspiracy theories fall short when applied to the system of belief known as QAnon. This paper first examines the previous literature that laid down a basis for understanding the nature of conspiracies and isolates aspects of the QAnon canon to delineate and test the predictors of belief. The data for this research were obtained from the 2020 wave of the Chapman Survey of American Fears (CSAF) conducted by the Earl Babbie Research Center at Chapman University. Layered crosstabulation tests and multiple linear regression results find that conspiratorial thinking outweighs partisanship when predicting QAnon belief. Strikingly, despite QAnon being tied to former President Donald Trump and crosstabulation results revealing a greater likelihood for Trump voters to believe in QAnon, the divide between Republican and Democrat voters was not very large, and all variables relating strictly to political orientation were statistically insignificant. This implies that QAnon is not a partisan conspiracy theory but rather an entirely new breed of hybrid conspiracy based around Donald Trump. The second part of my paper uses open-source intelligence methods to code and analyze content selected from nine alternative media messaging forums via the platform Telegram, each of which ranges from 3,000 to over 200,000 active users. Over seven hundred messages are synthesized in order to isolate specific elements of the belief system, identify trends, and better understand precisely what constitutes the conspiracy and what tools are utilized to drive belief and inspire radical action.

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