2021
DOI: 10.1007/s11192-020-03824-8
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A topic network analysis of the system turn in the environmental sciences

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Cited by 6 publications
(1 citation statement)
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“…In analyzing two corpora, comprising articles from large US dailies, we first estimate two Correlated Topic Models that identify the latent semantic structure of news reporting on Covid-19 and the swine flu. Second, we use these models to generate semantic networks, where nodes are topics and arcs represent inter-topic correlations (Rabitz et al 2021). The resulting models are iconic in semiotic terminology (Kralemann and Lattmann 2013) since they represent their objects through similarity to bring some of the objects' features to the fore.…”
Section: Models: Topic Semantic Iconicmentioning
confidence: 99%
“…In analyzing two corpora, comprising articles from large US dailies, we first estimate two Correlated Topic Models that identify the latent semantic structure of news reporting on Covid-19 and the swine flu. Second, we use these models to generate semantic networks, where nodes are topics and arcs represent inter-topic correlations (Rabitz et al 2021). The resulting models are iconic in semiotic terminology (Kralemann and Lattmann 2013) since they represent their objects through similarity to bring some of the objects' features to the fore.…”
Section: Models: Topic Semantic Iconicmentioning
confidence: 99%