Using ATLAS.ti to interpret keyword co-occurrence analysis: a case study on the representation of <i>vaccin*</i> across pseudoscience and conspiracy websites

Abstract In this study we use ATLAS.ti to interpret the results of a keyword co-occurrence analysis (KCA) of fake vaccination news. Specifically, KCA is used to uncover the most dominant patterns of co-occurring keywords across a corpus of 37,676 texts from 235 pseudoscience and conspiracy websites that mention vaccin* . KCA enables researchers to examine linguistic patterns of fake news from multiple angles, including discourse, register, style, and attitude. Yet, the interpretation of KCA can be time-consuming, especially when texts are long. Consequently, in this study, we leverage ATLAS.ti’s code co-occurrence analysis functionality, which streamlines and accelerates the interpretation of KCA results by providing access to extended concordances that highlight the patterns of keyword co-occurrence. Taking the second most prominent dimension as a demonstration, we interpret this pattern of keyword variation across our vaccination corpus as distinguishing texts that are questioning the COVID-19 pandemic, especially in relation to higher power control, from texts that are discussing childhood vaccines, especially with respect to the dangers they pose. The implications of these linguistic repertoires in relation to fake news and anti-science strategies are discussed.