I Want to Believe (but the Vocabulary Changed): Measuring the Semantic Structure and Evolution of Conspiracy Theories

A team of researchers—Manisha Keim, Sarmad Chandio, Osama Khalid, and Rishab Nithyanand—has published a novel study that uses computational linguistics to track the evolution of conspiracy theories in online discourse. Their paper, 'I Want to Believe (but the Vocabulary Changed): Measuring the Semantic Structure and Evolution of Conspiracy Theories,' moves beyond simply tracking keywords to analyze how the underlying meaning of conspiracy-related language changes over time. The core of their methodology involves treating conspiracy theories as 'semantic objects' within a language space, which allows for a more nuanced analysis than counting mentions of specific terms like 'QAnon' or 'chemtrails.'

To map this evolution, the researchers analyzed a massive dataset of 169.9 million comments from Reddit's r/politics subreddit, spanning the decade from 2012 to 2022. They employed aligned word embeddings, a type of AI model that represents words as vectors, to compare the 'semantic neighborhoods' of conspiracy-related language across different time periods. This technique allows them to see if the concepts associated with a conspiracy theory remain stable, expand to include new ideas, contract, or get entirely replaced by new terminology while retaining a similar core meaning.

The study's key finding is that conspiracy theories evolve in complex, non-uniform ways that are invisible to traditional keyword tracking. For instance, a theory might maintain a stable core belief while the specific vocabulary used to discuss it completely changes, or its meaning might expand to absorb adjacent political narratives. This research provides a powerful new framework for researchers, platform moderators, and policymakers to understand not just the spread, but the morphological adaptation of harmful online narratives, enabling more effective detection and intervention strategies.